Podcasts about olap

In this episode of TheHeleyCast, I sit down with Jake A. Merrick—radio personality, father, and a potential future governor of Oklahoma. We dive into Jake's personal background and beliefs, and explore some of the most pressing issues facing the state. We cover a wide range of topics, including: The rise of charter schools and the state of Oklahoma's education systemWhy Jake and his family choose homeschooling over public or private school optionsOklahoma's status as the lowest-taxed state for oil companies—and what that means for our economyThe complexities of tribal agreements, casino revenue, and state-tribal relationsA fun closer: pheasant hunting, OLAP land access, and the rising cost of licenses in OklahomaThis is a conversation packed with insight, controversy, and a few laughs along the way. Don't miss it!Anything helps, guys!Donate @Venmo: (@theheleycast)Cashapp: ($danielheley)Social Media:TikTok: (@theheleycast)Facebook: TheHeleyCastTwitter: @TheHeleyCastInstagram: @forgot_my_heleysBrought to you by Heley Entertainment.Become a supporter of this podcast: https://www.spreaker.com/podcast/theheleycast--4329275/support.

The ClickHouse® project is a rising star in observability and analytics, challenging performance conventions with its breakneck speed. This open source OLAP column store, originally developed at Yandex to power their web analytics platform at massive scale, has quickly evolved into one of the hottest open source observability data stores around. Its published performance benchmarks have been the topic of conversation, outperforming many legacy databases and setting a new bar for fast queries over large volumes of data.Our guest for this episode is Robert Hodges, CEO of Altinity — the second largest contributor to the ClickHouse project. With over 30 years of experience in databases, Robert brings deep insights into how ClickHouse is challenging legacy databases at scale. We'll also explore Altinity's just-launched groundbreaking open source project—Project Antalya—which extends ClickHouse with Apache Iceberg shared storage, unlocking dramatic improvements in both performance and cost efficiency. Think 90% reductions in storage costs and 10 to 100x faster queries, all without requiring any changes to your existing applications.The episode was live-streamed on 20 May 2025 and the video is available at https://www.youtube.com/watch?v=VeyTL2JlWp0You can read the recap post: https://medium.com/p/2004160b2f5e/ OpenObservability Talks episodes are released monthly, on the last Thursday of each month and are available for listening on your favorite podcast app and on YouTube.We live-stream the episodes on Twitch and YouTube Live - tune in to see us live, and chime in with your comments and questions on the live chat.⁠⁠https://www.youtube.com/@openobservabilitytalks⁠  https://www.twitch.tv/openobservability⁠Show Notes:00:00 - Intro01:38 - ClickHouse elevator pitch02:46 - guest intro04:48 - ClickHouse under the hood08:15 - SQL and the database evolution path 11:20 - the return of SQL16:13 - design for speed 17:14 - use cases for ClickHouse19:18 - ClickHouse ecosystem22:22 - ClickHouse on Kubernetes 31:45 - know how ClickHouse works inside to get the most out of it 38:59 - ClickHouse for Observability46:58 - Project Antalya55:03 - Kubernetes 1.33 release55:32 - OpenSearch 3.0 release56:01 - New Permissive License for ML Models Announced by the Linux Foundation57:08 - OutroResources:ClickHouse on GitHub: https://github.com/ClickHouse/ClickHouse Shopify's Journey to Planet-Scale Observability: https://medium.com/p/9c0b299a04ddProject Antalya: https://altinity.com/blog/getting-started-with-altinitys-project-antalya https://cmtops.dev/posts/building-observability-with-clickhouse/ Kubernetes 1.33 release highlights: https://www.linkedin.com/feed/update/urn:li:activity:7321054742174924800/ New Permissive License for Machine Learning Models Announced by the Linux Foundation: https://www.linkedin.com/feed/update/urn:li:share:7331046183244611584  Opensearch 3.0 major release: https://www.linkedin.com/posts/horovits_opensearch-activity-7325834736008880128-kCqrSocials:Twitter:⁠ https://twitter.com/OpenObserv⁠YouTube: ⁠https://www.youtube.com/@openobservabilitytalks⁠Dotan Horovits============X (Twitter): @horovitsLinkedIn: www.linkedin.com/in/horovitsMastodon: @horovits@fosstodonBlueSky: @horovits.bsky.socialRobert Hodges=============LinkedIn: https://www.linkedin.com/in/berkeleybob2105/ 

Join us for a deep dive into the world of databases with CMU professor Andy Pavlo. We discuss everything from OLTP vs. OLAP, the challenges of distributed databases, and why cloud-native databases require a fundamentally different approach than legacy systems. We discuss modern Vector Databases, RAG, Embeddings, Text to SQL and industry trends.You can follow Andy's work on:Blue Sky Youtube What's New In Data is a data thought leadership series hosted by John Kutay who leads data and products at Striim. What's New In Data hosts industry practitioners to discuss latest trends, common patterns for real world data patterns, and analytics success stories.

Global Agile Summit Preview: Implementing Agile Practices for Data and Analytics Teams with Henrik Reich In this BONUS Global Agile Summit preview episode, we dive into the world of Agile methodologies specifically tailored for data and analytics teams. Henrik Reich, Principal Architect at twoday Data & AI Denmark, shares his expertise on how data teams can adapt Agile principles to their unique needs, the challenges they face, and practical tips for successful implementation. The Evolution of Data Teams "Data and analytics work is moving more and more to be like software development." The landscape of data work is rapidly changing. Henrik explains how data teams are increasingly adopting software development practices, yet there remains a significant knowledge gap in effectively using certain tools. This transition creates both opportunities and challenges for organizations looking to implement Agile methodologies in their data teams. Henrik emphasizes that as data projects become more complex, the need for structured yet flexible approaches becomes critical. Dynamic Teams in the Data and Analytics World "When we do sprint planning, we have to assess who is available. Not always the same people are available." Henrik introduces the concept of "dynamic teams," particularly relevant in consulting environments. Unlike traditional Agile teams with consistent membership, data teams often work with fluctuating resources. This requires a unique approach to sprint planning and task assignment. Henrik describes how this dynamic structure affects team coordination, knowledge sharing, and project continuity, offering practical strategies for maintaining momentum despite changing team composition. Customizing Agile for Data and Analytics Teams "In data and analytics, tools have ignored agile practices for a long time." Henrik emphasizes that Agile isn't a one-size-fits-all solution, especially for data teams. He outlines the unique challenges these teams face: Team members have varying expectations based on their backgrounds Experienced data professionals sometimes skip quality practices Traditional data tools weren't designed with Agile methodologies in mind When adapting Agile for data teams, Henrik recommends focusing on three key areas: People and their expertise Technology selection Architecture decisions The overarching goal remains consistent: "How can we deliver as quickly as possible, and keep the good mood of the team?" Implementing CI/CD in Data Projects "Our first approach is to make CI/CD available in the teams." Continuous Integration and Continuous Deployment (CI/CD) practices are essential but often challenging to implement in data teams. Henrik shares how his organization creates "Accelerators" - tools and practices that enable teams to adopt CI/CD effectively. These accelerators address both technological requirements and new ways of working. Through practical examples, he demonstrates how teams can overcome common obstacles, such as version control challenges specific to data projects. In this segment, we refer to the book How to Succeed with Agile Business Intelligence by Raphael Branger. Practical Tips for Agile Adoption "Start small. Don't ditch scrum, take it as an inspiration." For data teams looking to adopt Agile practices, Henrik offers pragmatic advice: Begin with small, manageable changes Use established frameworks like Scrum as inspiration rather than rigid rules Practice new methodologies together as a team to build collective understanding Adapt processes based on team feedback and project requirements This approach allows data teams to embrace Agile principles while accounting for their unique characteristics and constraints. The Product Owner Challenge "CxOs are the biggest users of these systems." A common challenge in data teams is the emergence of "accidental product owners" - individuals who find themselves in product ownership roles without clear preparation. Henrik explains why this happens and offers solutions: Clearly identify who owns the project from the outset Consider implementing a "Proxy PO" role between executives and Agile data teams Recognize the importance of having the right stakeholder engagement for requirements gathering and feedback Henrik also highlights the diversity within data teams, noting there are typically "people who code for living, and people who live for coding." This diversity presents both challenges and opportunities for Agile implementation. Fostering Creativity in Structured Environments "Use sprint goals to motivate a team, and help everyone contribute." Data work often requires creative problem-solving - something that can seem at odds with structured Agile frameworks. Henrik discusses how to balance these seemingly conflicting needs by: Recognizing individual strengths within the team Organizing work to leverage these diverse abilities Using sprint goals to provide direction while allowing flexibility in approach This balanced approach helps maintain the benefits of Agile structure while creating space for the creative work essential to solving complex data problems. About Henrik Reich Henrik is a Principal Architect and developer in the R&D Department at twoday Data & AI Denmark. With deep expertise in OLTP and OLAP, he is a strong advocate of Agile development, automation, and continuous learning. He enjoys biking, music, technical blogging, and speaking at events on data and AI topics. You can link with Henrik Reich on LinkedIn and follow Henrik Reich's blog.

In this episode, Lois Houston and Nikita Abraham chat with MySQL expert Perside Foster on the importance of keeping MySQL performing at its best. They discuss the essential tools for monitoring MySQL, tackling slow queries, and boosting overall performance.   They also explore HeatWave, the powerful real-time analytics engine that brings machine learning and cross-cloud flexibility into MySQL.   MySQL 8.4 Essentials: https://mylearn.oracle.com/ou/course/mysql-84-essentials/141332/226362 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   ----------------------------------------------------------   Episode Transcript:   00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me today is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! In our last two episodes, we spoke about MySQL backups, exploring their critical role in data recovery, error correction, data migration, and more. Lois: Today, we're switching gears to talk about monitoring MySQL instances. We'll also explore the features and benefits of HeatWave with Perside Foster, a MySQL Principal Solution Engineer at Oracle. 01:02 Nikita: Hi, Perside! We're thrilled to have you here for one last time this season. So, let's start by discussing the importance of monitoring systems in general, especially when it comes to MySQL. Perside: Database administrators face a lot of challenges, and these sometimes appear in the form of questions that a DBA must answer. One of the most basic question is, why is the database slow? To address this, the next step is to determine which queries are taking the longest. Queries that take a long time might be because they are not correctly indexed. Then we get to some environmental queries or questions. How can we find out if our replicas are out of date? If lag is too much of a problem? Can I restore my last backup? Is the database storage likely to fill up any time soon? Can and should we consider adding more servers and scaling out the system? And when it comes to users and making sure they're behaving correctly, has the database structure changed? And if so, who did it and what did they do? And more generally, what security issues have arisen? How can I see what has happened and how can I fix it? Performance is always at the top of the list of things a DBA worries about. The underlying hardware will always be a factor but is one of the things a DBA has the least flexibility with changing over the short time. The database structure, choice of data types and the overall size of retained data in the active data set can be a problem. 03:01 Nikita: What are some common performance issues that database administrators encounter? Perside: The sort of SQL queries that the application runs can be an issue. 90% of performance problems come from the SQL index and schema group.  03:18 Lois: Perside, can you give us a checklist of the things we should monitor? Perside: Make sure your system is working. Monitor performance continually. Make sure replication is working. Check your backup. Keep an eye on disk space and how it grows over time. Check when long running queries block your application and identify those queries. Protect your database structure from unauthorized changes. Make sure the operating system itself is working fine and check that nothing unusual happened at that level. Keep aware of security vulnerabilities in your software and operating system and ensure that they are kept updated. Verify that your database memory usage is under control. 04:14 Lois: That's a great list, Perside. Thanks for that. Now, what tools can we use to effectively monitor MySQL?     Perside: The slow query log is a simple way to monitor long running queries. Two variables control the log queries. Long_query_time. If a query takes longer than this many seconds, it gets logged. And then there's min_exam_row_limit. If a query looks at more than this many rows, it gets logged. The slow query log doesn't ordinarily record administrative statements or queries that don't use indexes. Two variables control this, log_slow_admin_statements and log_queries_not_using_indexes. Once you have found a query that takes a long time to run, you can focus on optimizing the application, either by limiting this type of query or by optimizing it in some way. 05:23 Nikita: Perside, what tools can help us optimize slow queries and manage data more efficiently? Perside: To help you with processing the slow query log file, you can use the MySQL dump slow command to summarize slow queries. Another important monitoring feature of MySQL is the performance schema. It's a system database that provides statistics of how MySQL executes at a low level. Unlike user databases, performance schema does not persist data to disk. It uses its own storage engine that is flushed every time we start MySQL. And it has almost no interaction with the storage media, making it very fast. This performance information belongs only to the specific instance, so it's not replicated to other systems. Also, performance schema does not grow infinitely large. Instead, each row is recorded in a fixed size ring buffer. This means that when it's full, it starts again at the beginning. The SYS schema is another system database that's strongly related to performance schema. 06:49 Nikita: And how can the SYS schema enhance our monitoring efforts in MySQL? Perside: It contains helper objects like views and stored procedures. They help simplify common monitoring tasks and can help monitor server health and diagnose performance issues. Some of the views provide insights into I/O hotspots, blocking and locking issues, statements that use a lot of resources in various statistics on your busiest tables and indexes. 07:26 Lois: Ok… can you tell us about some of the features within the broader Oracle ecosystem that enhance our ability to monitor MySQL? Perside: As an Oracle customer, you also have access to Oracle Enterprise Manager. This tool supports a huge range of Oracle products. And for MySQL, it's used to monitor performance, system availability, your replication topology, InnoDB performance characteristics and locking, bad queries caught by the MySQL Enterprise firewall, and events that are raised by the MySQL Enterprise audit. 08:08 Nikita: What would you say are some of the standout features of Oracle Enterprise Manager? Perside: When you use MySQL in OCI, you have access to some really powerful features. HeatWave MySQL enables continuous monitoring of query statistics and performance. The health monitor is part of the MySQL server and gathers raw data about the performance of queries. You can see summaries of this information in the Performance Hub in the OCI Console. For example, you can see average statement latency or top 100 statements executed. MySQL metrics lets you drill in with your own custom monitoring queries. This works well with existing OCI features that you might already know. The observability and management framework lets you filter by resource type and across several dimensions. And you can configure OCI alarms to be notified when some condition is reached. 09:20 Lois: Perside, could you tell us more about MySQL metrics? Perside: MySQL metrics uses the raw performance data gathered by the health monitor to measure the important characteristic of your servers. This includes CPU and storage usage and information relevant to your database connection and queries executed. With MySQL metrics, you can create your own custom monitoring queries that you can use to feed graphics. This gives you an up to the minute representation of all the performance characteristics that you're interested in. You can also create alarms that trigger on some performance condition. And you can be notified through the OCI alarms framework so that you can be aware instantly when you need to deal with some issue.  10:22 Are you keen to stay ahead in today's fast-paced world? We've got your back! Each quarter, Oracle rolls out game-changing updates to its Fusion Cloud Applications. And to make sure you're always in the know, we offer New Features courses that give you an insider's look at all of the latest advancements. Don't miss out! Head over to mylearn.oracle.com to get started. 10:47 Nikita: Welcome back! Now, let's dive into the key features of HeatWave, the cloud service that integrates with MySQL. Can you tell us what HeatWave is all about? Perside: HeatWave is the cloud service for MySQL. MySQL is the world's leading database for web applications. And with HeatWave, you can run your online transaction processing or OLTP apps in the cloud. This gives you all the benefits of cloud deployments while keeping your MySQL-based web application running just like they would on your own premises. As well as OLTP applications, you need to run reports with Business Intelligence and Analytics Dashboards or Online Analytical Processing, or OLAP reports. The HeatWave cluster provides accelerated analytics queries without requiring extraction or transformation to a separate reporting system. This is achieved with an in-memory analytics accelerator, which is part of the HeatWave service. In addition, HeatWave enables you to create Machine Learning models to embed artificial intelligence right there in the database. The ML accelerator performs classification, regression, time-series forecasting, anomaly detection, and other functions provided by the various models that you can embed in your architecture. HeatWave can also work directly with storage outside the database. With HeatWave Lakehouse, you can run queries directly on data stored in object storage in a variety of formats without needing to import that data into your MySQL database. 12:50 Lois: With all of these exciting features in HeatWave, Perside, what core MySQL benefits can users continue to enjoy? Perside: The reason why you chose MySQL in the first place, it's still a relational database and with full transactional support, low latency, and high throughput for your online transaction processing app. It has encryption, compression, and high availability clustering. It also has the same large database support with up to 256 terabytes support. It has advanced security features, including authentication, data masking, and database firewall. But because it's part of the cloud service, it comes with automated patching, upgrades, and backup. And it is fully supported by the MySQL team. 13:50 Nikita: Ok… let's get back to what the HeatWave service entails. Perside: The HeatWave service is a fully managed MySQL. Through the web-based console, you can deploy your instances and manage backups, enable high availability, resize your instances, create read replicas, and perform many common administration tasks without writing a single line of SQL. It brings with it the power of OCI and MySQL Enterprise Edition. As a managed service, many routine DBA tests are automated. This includes keeping the instances up to date with the latest version and patches. You can run analytics queries right there in the database without needing to extract and transform your databases, or load them in another dedicated analytics system. 14:52 Nikita: Can you share some common use cases for HeatWave? Perside: You have your typical OLTP workloads, just like you'd run on prem, but with the benefit of being managed in the cloud. Analytic queries are accelerated by HeatWave. So your reporting applications and dashboards are way faster. You can run both OLTP and analytics workloads from the same database, keeping your reports up to date without needing a separate reporting infrastructure. 15:25 Lois: I've heard a lot about HeatWave AutoML. Can you explain what that is? Perside: HeatWave AutoML enables in-database artificial intelligence and Machine Learning. Externally sourced data stores, such as sensor data exported to CSV, can be read directly from object store. And HeatWave generative AI enables chatbots and LLM content creation. 15:57 Lois: Perside, tell us about some of the key features and benefits of HeatWave. Perside: Autopilot is a suite of AI-powered tools to improve the performance and applicability of your HeatWave queries. Autopilot includes two features that help cut costs when you provision your service. There's auto provisioning and auto shape prediction. They analyze your existing use case and tell you exactly which shape you must provision for your nodes and how many nodes you need. Auto parallel loading is used when you import data into HeatWave. It splits the import automatically into an optimum number of parallel streams to speed up your import. And then there's auto data placement. It distributes your data across the HeatWave cluster node to improve your query retrieval performance. Auto encoding chooses the correct data storage type for your string data, cutting down storage and retrieval time. Auto error recovery automatically recovers a fail node and reloads data if that node becomes unresponsive. Auto scheduling prioritizes incoming queries intelligently. An auto change propagation brings data optimally from your DB system to the acceleration cluster. And then there's auto query time estimation and auto query plan improvement. They learn from your workload. They use those statistics to perform on node adaptive optimization. This optimization allows each query portion to be executed on every local node based on that node's actual data distribution at runtime. Finally, there's auto thread pooling. It adjusts the enterprise thread pool configuration to maximize concurrent throughput. It is workload-aware, and minimizes resource contention, which can be caused by too many waiting transactions. 18:24 Lois: How does HeatWave simplify analytics within MySQL and with external data sources? Perside: HeatWave in Oracle Cloud Infrastructure provides all the features you need for analytics, all in one system. Your classic OLTP application run on the MySQL database that you know and love, provision in a DB system. On-line analytical processing is done right there in the database without needing to extract and load it to another analytic system. With HeatWave Lakehouse, you can even run your analytics queries against external data stores without loading them to your DB system. And you can run your machine learning models and LLMs in the same HeatWave service using HeatWave AutoML and generative AI. HeatWave is not just available in Oracle Cloud Infrastructure. If you're tied to another cloud vendor, such as AWS or Azure, you can use HeatWave from your applications in those cloud too, and at a great price. 19:43 Nikita: That's awesome! Thank you, Perside, for joining us throughout this season on MySQL. These conversations have been so insightful. If you're interested in learning more about the topics we discussed today, head over to mylearn.oracle.com and search for the MySQL 8.4: Essentials course.  Lois: This wraps up our season on the essentials of MySQL. But before we go, we just want to remind you to write to us if you have any feedback, questions, or ideas for future episodes. Drop us an email at ou-podcast_ww@oracle.com. That's ou-podcast_ww@oracle.com. Nikita: Until next time, this is Nikita Abraham… Lois: And Lois Houston, signing off! 20:33 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

David Thionnet is a lifelong quail hunter from Oklahoma.  David and Travis share stories from their action-packed bobwhite quail hunt, David explains the quail hunting tradition that hooked him as a young child and his love of the covey rise, why numbers are up this year, habitat that helps and hurts quail populations, hunting with 6 bird dogs at one time, OLAP public access program, Quail Coalition, sharing in the hunting traditions, more stories from Beadie the blind bird hunter, and plenty more! Presented by: Walton's (https://www.waltons.com/) OnX Maps (https://www.onxmaps.com/) Aluma Trailers (https://www.alumaklm.com) Lucky Duck Premium Kennels (https://www.luckyduck.com/) & Hoksey Native Seeds (https://hokseynativeseeds.com)

David Thionnet is a lifelong quail hunter from Oklahoma.  David and Travis share stories from their action-packed bobwhite quail hunt, David explains the quail hunting tradition that hooked him as a young child and his love of the covey rise, why numbers are up this year, habitat that helps and hurts quail populations, hunting with 6 bird dogs at one time, OLAP public access program, Quail Coalition, sharing in the hunting traditions, more stories from Beadie the blind bird hunter, and plenty more! Presented by: Walton's (https://www.waltons.com/) OnX Maps (https://www.onxmaps.com/) Aluma Trailers (https://www.alumaklm.com) Lucky Duck Premium Kennels (https://www.luckyduck.com/) & Hoksey Native Seeds (https://hokseynativeseeds.com)

Like every other kind of technology, when it comes to databases there's no one-size-fits-all solution that's going to be the best thing for the job every time. That's what drives innovation and new solutions. It's ultimately also the story behind DuckDB, an open source relational database specifically designed for the demands of online analytical processing (OLAP), and particularly useful for data analysts, scientists and engineers.  To get a deeper understanding of DuckDB and how the product has developed, on this episode of the Technology Podcast hosts Ken Mugrage and Lilly Ryan are joined by Thoughtworker Ned Letcher and Thoughtworks alumnus Simon Aubury. Ned and Simon explain the thinking behind DuckDB, the design decisions made by the project and how its being used by data practitioners in the wild. Learn more about DuckDB: https://duckdb.org/why_duckdb.html    

How has the cloud transformed the way we work with data? While at Build in Seattle, Richard sat down with Arun Ulag, Microsoft CVP of Azure Data, to discuss how the cloud has transformed how we work with data. The pre-cloud practice of extract-transform-and-load into OLAP cubes has given way to the data lake - you don't need to pre-process data if you have all the compute you need on demand. Arun goes further into empowering analysts using tools like PowerBI - but the key is access to data. With Microsoft Fabric, data lives in OneLake - or anywhere through links! Today, the data analytics landscape spans different product stacks and clouds - but all are available to learn more about your business!Links:PowerBIPivot Tables in ExcelOne LakeApache IcebergSnowflakeDatabricksRecorded May 22, 2024

В этом выпуске: все подробности об использовании солнечных панелей на крыше своего дома, свежие видео с конференции pgconf.dev 2024, свежий котик и свежие темы наших слушателей. Шоуноты: [00:05:50] Чему мы научились за неделю [00:21:16] [Одной строкой] PostgreSQL Hacking Workshop [00:25:34] [Одной строкой] A shallow survey of OLAP and HTAP query engines [00:28:39] Tesla Powerwall [01:08:38]… Читать далее →

DuckDB nasce dai limiti di SQLite ed aggiunge tutta una serie di funzionalità pensate per poter analizzare dati provenienti da fonti diverse/esterne andando a fruttare la sintassi SQL.Nel sito viene riportato come "DuckDB is a fast in-process analytical database"...ne parliamo in questo episodio.https://duckdb.org/https://duckdb.org/why_duckdbhttps://duckdb.net/  https://github.com/Giorgi/DuckDB.NEThttps://www.youtube.com/watch?v=_a9spwyb1FU#duckdb #sqllite #olap #dotnetinpillole #podcast #dotnet

In this video I speak with Andrew Lamb, Staff Software Engineer @Influxdb. We discuss FDAP (Flight, DataFusion, Arrow, Parquet) stack for modern OLAP database system design. Andrew shared some insights into why the FDAP stack is so powerful in designing and implementing a modern OLAP database. Chapters: 00:00 Introduction 01:48 Understanding Analytics: Transactional vs Analytical Databases 04:41 The Genesis and Goals of the FDAP Stack 09:31 Decoding FDAP: Flight, Data Fusion, Arrow, and Parquet 12:40 Apache Parquet: Revolutionizing Columnar Storage 17:18 Apache Arrow: The In-Memory Game Changer 23:51 Interoperability and Migration with Apache Arrow 27:10 Comparing Apache Parquet and Arrow 28:26 Exploring Data Mutability in Analytic Systems 29:19 Handling Data Updates and Deletions 29:24 The Role of Immutable Storage in Analytics 30:42 Optimizing Data Storage and Mutation Strategies 34:20 Introducing Flight: Simplifying Data Transfer 35:02 Deep Dive into Flight's Benefits and SQL Support 39:20 Unpacking Data Fusion's SQL Support and Extensibility 46:12 The Interplay of FDAP Components in Analytics 51:49 Future Directions and Innovations in Data Analytics 56:04 Concluding Thoughts on FDAP and Its Impact FDAP Stack: https://www.influxdata.com/glossary/fdap-stack/ FDAP Blog: https://www.influxdata.com/blog/flight-datafusion-arrow-parquet-fdap-architecture-influxdb/ InfluxDB: https://www.influxdata.com/ Follow me on Linkedin and Twitter: https://www.linkedin.com/in/kaivalyaapte/ and https://twitter.com/thegeeknarrator If you like this episode, please hit the like button and share it with your network. Also please subscribe if you haven't yet. Database internals series: https://youtu.be/yV_Zp0Mi3xs Popular playlists: Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA- Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17 Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_d Modern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsN Stay Curios! Keep Learning! #datafusion #parquet #sql #OLAP #apachearrow #database #systemdesign

In this video I speak with Philippe Noël, about ParadeDB, which is an Elasticsearch alternative built on Postgres, modernizing the features of Elasticsearch's product suite, starting with real-time search and analytics. I hope you will enjoy and learn about the product. Chapters: 00:00 Introduction 01:12 Challenges with Elasticsearch and the Need for ParadeDB 02:29 Why Postgres? 06:30 Technical Details of ParadeDB's Search Functionality 18:25 Analytics Capabilities of ParadeDB 24:00 Understanding ParadeDB Queries and Transactions 24:22 Application Logic and Data Workflows 25:14 Using PG Cron for Data Migration 30:05 Scaling Reads and Writes in Postgres 31:53 High Availability and Distributed Systems 34:31 Isolation of Workloads 39:38 Database Upgrades and Migrations 41:21 Using ParadeDB Extensions and Distributions 43:02 Observability and Monitoring 44:42 Upcoming Features and Roadmap 46:34 Final Thoughts Important links: Links: GitHub: https://github.com/paradedb/paradedb Website: https://paradedb.com Docs: https://docs.paradedb.com/ Blog: https://blog.paradedb.com Follow me on Linkedin and Twitter: https://www.linkedin.com/in/kaivalyaapte/ and https://twitter.com/thegeeknarrator If you like this episode, please hit the like button and share it with your network. Also please subscribe if you haven't yet. Database internals series: https://youtu.be/yV_Zp0Mi3xs Popular playlists: Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA- Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17 Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_d Modern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsN Stay Curios! Keep Learning! #postgresql #datafusion #parquet #sql #OLAP #apachearrow #database #systemdesign #elasticsearch

PostgreSQL is an incredible general-purpose database, but it can't do everything. Every design decision is a tradeoff, and inevitably some of those tradeoffs get fundamentally baked into the way it's built. Take storage for instance - Postgres tables are row-oriented; great for row-by-row access, but when it comes to analytics, it can't compete with a dedicated OLAP database that uses column-oriented storage. Or can it?Joining me this week is Philippe Noël of ParadeDB, who's going to take us on a tour of Postgres' extension mechanism, from creating custom functions and indexes to Rust code that changes the way Postgres stores data on disk. In his journey to bring Elasticsearch's strengths to Postgres, he's gone all the way down to raw datafiles and back through the optimiser to teach a venerable old dog some new data-access tricks. –ParadeDB: https://paradedb.comParadeDB on Twitter: https://twitter.com/paradedbParadeDB on Github: https://github.com/paradedb/paradedbpgrx (Postgres with Rust): https://github.com/pgcentralfoundation/pgrxTantivy (Rust FTS library): https://github.com/quickwit-oss/tantivyPgMQ (Queues in Postgres): https://tembo.io/blog/introducing-pgmqApache Datafusion: https://datafusion.apache.org/Lucene: https://lucene.apache.org/Kris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkins

Welcome back to an episode where we're talking Vectors, Vector Databases, and AI with Linpeng Tang, CTO and co-founder of MyScale. MyScale is a super interesting technology. They're combining the best of OLAP databases with Vector Search. The project started back in 2019 where they forked ClickHouse and then adapted it to support Vector Storage, Indexing, and Search. The really unique and cool thing is you get the familiarity and usability of SQL with the power of being able to compare the similarity between unstructured data. We think this has really fascinating use cases for analytics well beyond what we're seeing with other vector database technology that's mostly restricted to building RAG models for LLMs. Also, because it's built on ClickHouse, MyScale is massively scalable, which is an area that many of the dedicated vector databases actually struggle with. We cover a lot about how vector databases work, why they decided to build off of ClickHouse, and how they plan to open source the database. Timestamps 02:29 Introduction 06:22 Value of a Vector Database 12:40 Forking ClickHouse 18:53 Transforming Clickhouse into a SQL vector database 32:08 Data modeling 32:56 What data can be Vectorized 38:37 Indexing 43:35 Achieving Scale 46:35 Bottlenecks 48:41 MyScale vs other dedicated Vector Databases 51:38 Going Open Source 56:04 Closing thoughts

NService Bus This episode of The Modern .NET Show is supported, in part, by NServiceBus, the ultimate tool to build robust and reliable systems that can handle failures gracefully, maintain high availability, and scale to meet growing demand. Make sure you click the link in the show notes to learn more about NServiceBus. Show Notes Yeah. So what I was thinking the other day is that what we want is to concentrate on the business logic that we need to implement and spend as small as little time as possible configuring, installing and figuring out the tools and libraries that we are using for this specific task. Like our mission is to produce the business logic and we should try to minimize the time that we spend on the tools and libraries that enable us to build the software. —Giorgi Dalakishvili Welcome to The Modern .NET Show! Formerly known as The .NET Core Podcast, we are the go-to podcast for all .NET developers worldwide and I am your host Jamie "GaProgMan" Taylor. In this episode, I spoke with Giorgi Dalakishvili about Postgresql, DuckDB, and where you might use either of them in your applications. As Giorgi points out, .NET has support for SQL Server baked in, but there's also support for other database technologies too: Yes, there are many database technologies and just like you, for me, SQL Server was the default go to database for quite a long time because it's from Microsoft. All the frameworks and libraries work with SQL Server out of the box, and have usually better support for SQL Server than for other databases. But recently I have been diving into Postgresql, which is a free database and I discovered that it has many interesting features and I think that many .NET developers will be quite excited about these features. The are very useful in some very specific scenarios. And it also has a very good support for .NET. Nowadays there is a .NET driver for Postgres, there is a .NET driver for Entity Framework core. So I would say it's not behind SQL server in terms of .NET support or feature wise. —Giorgi Dalakishvili He also points out that our specialist skill as developers is not to focus on the tools, libraries, and frameworks, but to use what we have in our collective toolboxes to build the business logic that our customers, clients, and users desire of us. And along the way, he drops some knowledge on an essential NuGet package for those of us who are using Entity Framework.. So let's sit back, open up a terminal, type in dotnet new podcast and we'll dive into the core of Modern .NET. Supporting the Show If you find this episode useful in any way, please consider supporting the show by either leaving a review (check our review page for ways to do that), sharing the episode with a friend or colleague, buying the host a coffee, or considering becoming a Patron of the show. Full Show Notes The full show notes, including links to some of the things we discussed and a full transcription of this episode, can be found at: https://dotnetcore.show/season-6/from-net-to-DuckDB-unleashing-the-database-evolution-with-giorgi-dalakishvili/ Useful Links Giorgi's GitHub DuckDB .NET Driver Postgres Array data type Postgres Range data type DuckDB DbUpdateException EntityFramework.Exceptions JsonB data type Vector embeddings Cosine similarity Vector databases: Chroma qdrant pgvector pgvector .NET library OLAP queries parquet files Dapper DuckDB documentation Dapr DuckDB Wasm; run DuckDB in your browser GitHub Codespaces Connecting with Giorgi: on Twitter on LinkedIn on his website Supporting the show: Leave a rating or review Buy the show a coffee Become a patron Getting in touch: via the contact page joining the Discord Music created by Mono Memory Music, licensed to RJJ Software for use in The Modern .NET Show Remember to rate and review the show on  Apple Podcasts, Podchaser, or wherever you find your podcasts, this will help the show's audience grow. Or you can just share the show with a friend. And don't forget to reach out via our Contact page. We're very interested in your opinion of the show, so please get in touch. You can support the show by making a monthly donation on the show's Patreon page at: https://www.patreon.com/TheDotNetCorePodcast.

Highlights from this week's conversation include:Michael's background and journey in data (0:33)The origin story of Druid (2:39)Experiences and growth in Data (8:08)Druid's evolution (21:46)Druid's architectural decisions (26:32)The user experience (30:06)The developer experience (35:14)The evolution of BI tools (40:55)Data architecture and integration (47:53)AI's impact on BI (52:26)What would Mike be doing if he didn't work in data? (56:27)Final thoughts and takeaways (57:02)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Highlights from this week's conversation include:Tony's background and research focus (3:35)Challenges in academia and industry (6:15)Ph.D. student's routine (10:47)Academic paper review process (15:26)Aha moments in research (20:05)Academic lab structure (23:09)The decision to move from hardware to data research (24:43)Research focus on time series data management (27:40)Data modeling in time series and OLAP systems (32:01)Issues and potential solutions for parquet format (37:32)Role of external indices in parquet files (42:19)Tony's open source project (47:11)Final thoughts and takeaways (49:30)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Tan học, nhiều bạn sinh viên bắt đầu trở về nhà, người thì chuẩn bị cho các cuộc hẹn xả hơi sau một ngày học dài, người thì hối hả đi làm thêm. Theo kết quả khảo sát của Liên đoàn các hiệp hội sinh viên Pháp (FAGE) công bố trong tháng 1, trong số hơn 7500 sinh viên được hỏi có tới 41% khẳng định phải làm thêm bán thời gian để trang trải cho cuộc sống, trong đó 35% phải đi làm nhiều hơn 12 giờ một tuần. Các công việc làm thêm của sinh viên tại Pháp rất đa dạng, từ trợ giảng, hoạt náo viên đến phát tờ rơi, v.v trong đó ba lĩnh vực được sinh viên lựa chọn nhiều nhất theo báo cáo năm 2020 của trung tâm Quan sát Quốc gia về Đời sống sinh viên (OVE) (thực hiện 3 năm/lần) bao gồm : giữ trẻ, bán hàng/ thu ngân tại các cửa hàng, siêu thị và phục vụ/phụ bếp tại các nhà hàng. Dễ thấy đây đều là những công việc phổ thông, không yêu cầu quá nhiều về chuyên môn và thường không liên quan hoặc rất ít liên quan đến ngành học của sinh viên (56,7% sinh viên cho biết). Về tiền lương, theo báo cáo đăng ngày 14/08/2023 của Nghiệp đoàn Sinh viên (UNEF), mức lương làm thêm trung bình trong năm 2023 của sinh viên rơi vào khoảng 728 euro/tháng và thời gian làm việc trung bình là 21,1 giờ/tuần. Mức lương như vậy liệu có đủ để trang trải cuộc sống? Để trả lời được câu hỏi này, có lẽ ta cần nhìn vào mức chi tiêu trung bình hiện nay tại Pháp. Trong bài viết được đăng tải ngày 13/05/2023 về chi tiêu hàng tháng của người dân, đài France Bleu thống kê mỗi tháng một người tại Pháp sẽ phải trả trung bình 1195 euro cho những khoản chi tiêu “cố định”, bao gồm tiền nhà, tiền điện, nước, sưởi, tiền bảo hiểm, v.v. Con số này đã tăng 9% so với 6 tháng trước đó, tương đương khoảng 100 euro.Vẫn biết rằng sinh viên, đặc biệt là nhóm sinh viên dưới 26 tuổi, thường được hưởng rất nhiều ưu đãi và trợ cấp trong hầu hết các lĩnh vực từ đi lại, mua sắm đến nhà ở, nhưng khoảng cách giữa mức lương trung bình của sinh viên và mức chi tiêu trung bình tại Pháp quả thực vẫn không hề nhỏ. Còn nếu so sánh với số liệu năm 2021 từ Viện Thống kê và Nghiên cứu Kinh tế Quốc gia Pháp (Insee) đăng ngày 14/11/2023, nếu không có thêm bất cứ trợ cấp nào từ chính phủ và gia đình thì mức thu nhập trung bình như vậy từ việc làm thêm của sinh viên được coi là dưới cả ngưỡng nghèo khó. Cần phải nhắc lại rằng, theo Insee, mức nghèo đói của Pháp trong năm 2021 được tính từ khoảng 965 đến 1158 euro/tháng, tương đương 50-60% mức lương trung bình. Đi làm thêm tác động nhiều đến kết quả học tập. Theo khảo sát thường niên về đời sống sinh viên của OVE năm 2023, 31,7% sinh viên được hỏi cho rằng làm thêm bán thời gian kéo dài trên 6 tháng ảnh hưởng tiêu cực đến kết quả học tập, trong khi 45,6% cho rằng công việc này là nguyên nhân dẫn đến căng thẳng và áp lực trong cuộc sống. Ông Julien Berthaud, tác giả của cuốn Việc làm sinh viên, quá trình học tập và điều kiện sống, cảnh báo công việc làm thêm không liên quan đến ngành học và vượt quá 18 giờ/tuần có nguy cơ làm tăng tỉ lệ sinh viên bỏ dở việc học để đi làm. Và sau đó, sinh viên sẽ “nhận ra rằng các cơ hội thăng tiến giảm đi rất nhiều” và về lâu dài đa số họ sẽ chỉ có thể làm “những công việc không ổn định hoặc không yêu cầu trình độ”, bà Vanessa Pinto, giảng viên tại đại học Reims-Champagne-Ardennes, tác giả của cuốn Từ việc học đến việc làm, sinh viên và công việc làm thêm cho hay. Những bữa ăn “cân bằng” Một chế độ ăn uống cân bằng là điều mà nhiều sinh viên tại Pháp hiện đang theo đuổi, chỉ tiếc rằng yếu tố được cân bằng ở đây lại không phải là dinh dưỡng mà là tiền bạc. Bữa này ăn thì bữa sau nhịn, bữa sau ăn thì bữa sau nữa nhịn. Theo báo cáo của FAGE, cứ năm sinh viên tại Pháp thì lại có một người thường xuyên bỏ bữa để cân đối thu chi, trung bình có khoảng 3,5 bữa bị bỏ/tuần. Còn theo số liệu thống kê đăng ngày 12/09/2023 của hiệp hội hỗ trợ sinh viên COP1, 77% sinh viên tìm tới các sản phẩm có giá thành thấp hơn, 66% ưu tiên các sản phẩm giảm giá. Rau củ và hoa quả tươi cũng là các nhóm thực phẩm thường được ưu tiên loại khỏi danh sách đi chợ do giá cả không mấy thân thiện (49% sinh viên được hỏi cho biết). Là một sinh viên Pháp sống tại Marseille, Louise chia sẻ bản thân cũng gặp khó khăn trong việc quản lý chi tiêu khi giá cả ngày một tăng cao.  “Bình thường với chuyện ăn uống, mỗi tháng mình chi khoảng 140 euro, tức là khoảng 5 euro/ngày cho tiền mua đồ ăn vì ngân sách của mình chỉ như vậy. Số tiền còn lại thì mình dùng để chi cho các đồ dùng cá nhân khác. Mình cũng thường tới ăn tại căng tin của trường vì ở đây có các bữa ăn giá 1 euro dành cho sinh viên. Căng tin trường mình mở cả buổi tối, nhưng cuối tuần thì không. Vì vậy cuối tuần mình thường tự nấu ăn vì một vài loại rau củ và mì cũng không quá đắt.” Khi được hỏi liệu bản thân có gặp khó khăn gì trong việc chi tiêu với số tiền hiện có cho một tháng hay không, Louise cho biết “với những đồ dùng cơ bản, thiết yếu như đồ dùng học tập hay đồ ăn thì mình không gặp khó khăn. Nhưng với các hoạt động như tụ tập với bạn bè thì thực sự bị hạn chế nhiều. Vì vậy để cân đối chi tiêu, mình cũng thường lựa chọn các sản phẩm giảm giá, không nhất thiết phải có chất lượng quá cao. Tuy vậy nhưng thỉnh thoảng mình vẫn có thể mua được các loại thực phẩm sạch mà giá cả phải chăng. Mình cũng không thường xuyên đi ăn ở nhà hàng vì tốn kém quá. Mình cũng không lựa chọn đồ ăn chế biến sẵn trong siêu thị, giá của chúng thì không đắt nhưng thực sự không xứng đáng với chất lượng.” Câu chuyện an cư, khó càng thêm khóTheo kết quả khảo sát hàng năm của trung tâm theo dõi giá thuê nhà tại Paris và vùng phụ cận (OLAP) công bố vào tháng 09/2023, tính riêng trong quý một năm 2023, giá cho thuê nhà tư nhân chưa bao gồm đồ đạc đã tăng trung bình 2,4% tại Paris và các thành phố phụ cận. Dù theo trang bất động sản Locservice, mức tăng này được đánh giá là không quá cao nhờ vào nghị định áp mức giá trần của chính phủ nhằm giám sát việc tăng tiền thuê nhà hàng năm, nhưng giá nhà tại Paris vẫn cao gấp 168% so với các tỉnh thành phố khác tại Pháp. Tuy nhiên cũng theo khảo sát của Locservice, ngân sách trung bình dành cho việc thuê nhà hàng tháng của người dân lại thấp hơn 32 euro so với giá thuê nhà hiện tại. Điều này khiến cho những người mong muốn tìm một chỗ ở tại Paris, đặc biệt là sinh viên, nhóm đối tượng chiếm tới 36% số người tìm nhà tại thành phố này, gặp rất nhiều khó khăn. 65% sinh viên được hỏi cho rằng giá thuê nhà quá cao so với mức tài chính của họ. Helen, sinh viên Hy Lạp hiện đang sống và học tập tại Paris chia sẻ với RFI Việt ngữ những khó khăn mà bạn đã gặp phải khi tìm nhà tại thành phố đắt đỏ bậc nhất nước Pháp : “Thực sự rất khó để tìm được nhà ở đây vì khi bắt đầu tìm thì mình vẫn đang ở Hy Lạp. Mình tìm nhà khá sớm, ngay từ cuối tháng 6, đầu tháng 7 nhưng các phòng hầu như đã kín chỗ. Vấn đề là trên các trang web cho thuê, họ ghi là vẫn còn phòng nhưng khi mình gọi hỏi thì mới biết các phòng đều đã được thuê hết. Vậy nên cuối cùng mình đã phải bay từ Hy Lạp đến Paris để hẹn gặp trực tiếp các chủ nhà. Mình chọn căn phòng hiện tại vì nó cũng là lựa chọn cuối cùng mà mình có.” Khi được hỏi về giá thuê phòng, Helen cho biết “mức giá này thực sự khá đắt, nhất là đối với sinh viên mà phòng thì cũng không quá rộng.” Thay vì lựa chọn nhà ở tư nhân với mức giá đắt đỏ, sinh viên tại Pháp có thể tìm tới các ký túc xá được quản lý bởi các Crous (Trung tâm phục vụ sự nghiệp đại học và giáo dục phổ thông khu vực). Đây được đánh giá là loại hình nhà ở tiết kiệm nhất với chi phí dao động từ 200-500 euro/tháng cùng nhiều sự lựa chọn đa dạng. Các ký túc xá này cũng thường gần các trường đại học và các phương tiện giao thông công cộng. Vị trí hợp lý, giá cả phải chăng nhưng đáng tiếc là chỉ có chưa tới 6% sinh viên được ở tại các ký túc xá do Crous quản lý do khan hiếm phòng cho thuê. Hiện nay tại Pháp có tổng cộng 173 430 phòng ký túc xá Crous, nhưng lại có tới ba triệu sinh viên, chưa kể tới việc cơ sở vật chất tại các phòng ký túc xá ngày càng xuống cấp. Trả lời khảo sát của FAGE, sinh viên cho biết nhiều phòng hiện đang trong tình trạng hỏng lò sưởi, thường xuyên bị cắt nước nóng, trên tường nhiều những vết nứt và là nơi trú ngụ yêu thích của các loài gián và rệp. Hỗ trợ của chính phủ, đa dạng nhưng khó tiếp cận Để giải quyết tình hình khó khăn hiện tại của sinh viên, chính phủ Pháp trong năm ngoái đã đưa ra nhiều biện pháp hỗ trợ nhằm tăng số lượng cũng như tăng giá trị của các học bổng dành cho sinh viên có hoàn cảnh khó khăn. Theo bài viết của tạp chí sinh viên L'Étudiant đăng ngày 25/04/2023 của tác giả Etienne Gless và Clémentine Rigot, chính phủ đã chi 500 triệu euro để cải thiện hệ thống học bổng này. Như vậy theo dự kiến, trong năm học 2023-2024 sẽ có 144 000 sinh viên được nhận học bổng trợ cấp và số tiền trợ cấp mà sinh viên được nhận cũng sẽ tăng thêm khoảng 37 euro/tháng. Tuy nhiên theo bà Sarah Biche, phụ trách các hoạt động xã hội của FAGE, mức tăng này được đánh giá là “không mấy khác biệt”. Bà cũng cho biết hiện tại có nhiều bạn sinh viên thuộc vào nhóm sinh viên “chưa đủ khó khăn” để được nhận trợ cấp nhưng tình hình tài chính của họ cũng không cho phép họ có một cuộc sống đầy đủ và đúng nghĩa. Ngoài trợ cấp trên, mọi sinh viên tại Pháp, bất kể với quốc tịch nào, cũng đều có thể nhận được trợ cấp nhà ở của chính phủ. Song song với đó, chính phủ cũng đưa ra rất nhiều các khoản hỗ trợ khác cho sinh viên như hỗ trợ thu nhập tối thiểu cho sinh viên (RME), quỹ hỗ trợ khẩn cấp (FNAU) hay hỗ trợ di chuyển cho sinh viên theo học thạc sĩ, tiến sĩ (AMI), v.v. Đa dạng là vậy nhưng việc tiếp cận tới các chương trình này lại không hề dễ dàng.  Chỉ riêng với khoản trợ cấp nhà ở, một trong những khoản trợ cấp cơ bản và được phổ biến rộng rãi nhất, quy trình đăng ký cũng đã khiến rất nhiều sinh viên phải đau đầu vì số lượng lớn giấy tờ cần nộp cùng thủ tục xử lý kéo dài. Nhiều sinh viên đã phải chờ đợi mòn mỏi từ vài tháng đến nửa năm cho tới khi hồ sơ của mình được duyệt. Helen, sinh viên Hy Lạp tại Paris cho biết “Mình đã bắt đầu xin trợ cấp nhà ở từ tháng 8 năm ngoái nhưng đến giờ là tháng 1 mình vẫn chưa nhận được khoản tiền này. Kỳ lạ nhất là khi hồ sơ của mình chưa đầy đủ, CAF cũng không thông báo gì với mình. Chỉ tới khi mãi không nhận được trợ cấp, mình gọi điện hỏi thì mới biết mình nộp thiếu một tờ giấy chứng nhận. Mình đã bổ sung và giờ thì mình lại tiếp tục chờ khoản tiền này. Không có tiền trợ cấp thực sự rất khó để có thể chi trả tiền nhà vì giá điện thì vẫn không ngừng tăng. Hiện giờ mình chỉ có thể cố gắng không sử dụng lò sưởi quá nhiều vì giá điện cao quá và cũng chỉ mua những đồ dùng thật sự cần thiết thôi.” Theo kế hoạch chính thức được bộ trưởng Đại học Pháp, bà Sylvie Retailleau công bố hôm 25/04/2023, trong năm 2024 ngân sách của chính phủ Pháp dành cho giảng dạy bậc đại học và sau đại học và ngân sách cho đời sống sinh viên sẽ tăng 469 triệu euro so với năm ngoái. Cụ thể, ngân sách dành cho chương trình 150 về Đào tạo và nghiên cứu đại học năm nay là 14,2 tỷ euro (tăng 273 triệu euro) và ngân sách dành cho chương trình 231 về Đời sống sinh viên là 3,3 tỷ euro (tăng 196 triệu euro). Có thể thấy chính phủ Pháp vẫn rất coi trọng giáo dục đại học và sau đại học và không ngừng đầu tư cho bậc học này cũng như cho đời sống sinh viên. Hy vọng rằng với những nỗ lực đó, tình hình đời sống của sinh viên trong năm nay sẽ phần nào được cải thiện. 

What is MySQL HeatWave? How do I get certified in it? Where do I start? Listen to Lois Houston and Nikita Abraham, along with MySQL Developer Scott Stroz, answer all these questions and more on this week's episode of the Oracle University Podcast. MySQL Document Store: https://oracleuniversitypodcast.libsyn.com/mysql-document-store Oracle MyLearn: https://mylearn.oracle.com/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X (formerly Twitter): https://twitter.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this  series of informative podcasts, we'll bring you foundational training on the most popular  Oracle technologies. Let's get started! 00:26 Nikita: Welcome to the Oracle University Podcast! I'm Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! For the last two weeks, we've been having really exciting discussions on everything AI. We covered the basics of artificial intelligence and machine learning, and we're taking a short break from that today to talk about the new MySQL HeatWave Implementation Associate Certification with MySQL Developer Advocate Scott Stroz. 00:59 Nikita: You may remember Scott from an episode last year where he came on to discuss MySQL Document Store. We'll post the link to that episode in the show notes so you can listen to it if you haven't already. Lois: Hi Scott! Thanks for joining us again. Before diving into the certification, tell us, what is MySQL HeatWave?  01:19 Scott: Hi Lois, Hi Niki. I'm so glad to be back. So, MySQL HeatWave Database Service is a fully managed database that is capable of running transactional and analytic queries in a single database instance. This can be done across data warehouses and data lakes. We get all the benefits of analytic queries without the latency and potential security issues of performing standard extract, transform, and load, or ETL, operations. Some other MySQL HeatWave database service features are automated system updates and database backups, high availability, in-database machine learning with AutoML, MySQL Autopilot for managing instance provisioning, and enhanced data security.  HeatWave is the only cloud database service running MySQL that is built, managed, and supported by the MySQL Engineering team. 02:14 Lois: And where can I find MySQL HeatWave? Scott: MySQL HeatWave is only available in the cloud. MySQL HeatWave instances can be provisioned in Oracle Cloud Infrastructure or OCI, Amazon Web Services (AWS), and Microsoft Azure. Now, some features though are only available in Oracle Cloud, such as access to MySQL Document Store. 02:36 Nikita: Scott, you said MySQL HeatWave runs transactional and analytic queries in a single instance. Can you elaborate on that? Scott: Sure, Niki. So, MySQL HeatWave allows developers, database administrators, and data analysts to run transactional queries (OLTP) and analytic queries (OLAP).  OLTP, or online transaction processing, allows for real-time execution of database transactions. A transaction is any kind of insertion, deletion, update, or query of data. Most DBAs and developers work with this kind of processing in their day-to-day activities.   OLAP, or online analytical processing, is one way to handle multi-dimensional analytical queries typically used for reporting or data analytics. OLTP system data must typically be exported, aggregated, and imported into an OLAP system. This procedure is called ETL as I mentioned – extract, transform, and load. With large datasets, ETL processes can take a long time to complete, so analytic data could be “old” by the time it is available in an OLAP system. There is also an increased security risk in moving the data to an external source. 03:56 Scott: MySQL HeatWave eliminates the need for time-consuming ETL processes. We can actually get real-time analytics from our data since HeatWave allows for OLTP and OLAP in a single instance. I should note, this also includes analytic from JSON data that may be stored in the database. Another advantage is that applications can use MySQL HeatWave without changing any of the application code. Developers only need to point their applications at the MySQL HeatWave databases. MySQL HeatWave is fully compatible with on-premise MySQL instances, which can allow for a seamless transition to the cloud. And one other thing. When MySQL HeatWave has OLAP features enabled, MySQL can determine what type of query is being executed and route it to either the normal database system or the in-memory database. 04:52 Lois: That's so cool! And what about the other features you mentioned, Scott? Automated updates and backups, high availability… Scott: Right, Lois. But before that, I want to tell you about the in-memory query accelerator. MySQL HeatWave offers a massively parallel, in-memory hybrid columnar query processing engine. It provides high performance by utilizing algorithms for distributed query processing. And this query processing in MySQL HeatWave is optimized for cloud environments.  MySQL HeatWave can be configured to automatically apply system updates, so you will always have the latest and greatest version of MySQL. Then, we have automated backups. By this, I mean MySQL HeatWave can be configured to provide automated backups with point-in-time recovery to ensure data can be restored to a particular date and time. MySQL HeatWave also allows us to define a retention plan for our database backups, that means how long we keep the backups before they are deleted. High availability with MySQL HeatWave allows for more consistent uptime. When using high availability, MySQL HeatWave instances can be provisioned across multiple availability domains, providing automatic failover for when the primary node becomes unavailable. All availability domains within a region are physically separated from each other to mitigate the possibility of a single point of failure. 06:14 Scott: We also have MySQL Lakehouse. Lakehouse allows for the querying of data stored in object storage in various formats. This can be CSV, Parquet, Avro, or an export format from other database systems. And basically, we point Lakehouse at data stored in Oracle Cloud, and once it's ingested, the data can be queried just like any other data in a database. Lakehouse supports querying data up to half a petabyte in size using the HeatWave engine. And this allows users to take advantage of HeatWave for non-MySQL workloads. MySQL AutoPilot is a part of MySQL HeatWave and can be used to predict the number of HeatWave nodes a system will need and automatically provision them as part of a cluster. AutoPilot has features that can handle automatic thread pooling and database shape predicting. A “shape” is one of the many different CPU, memory, and ethernet traffic configurations available for MySQL HeatWave. MySQL HeatWave includes some advanced security features such as asymmetric encryption and automated data masking at query execution. As you can see, there are a lot of features covered under the HeatWave umbrella! 07:31 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You'll find training on everything from cloud computing, database, and security to artificial intelligence and machine learning, all free to subscribers. So, what are you waiting for? Pick a topic, leverage the Oracle University Learning Community to ask questions, and then sit for your certification. Visit mylearn.oracle.com to get started.  08:02 Nikita: Welcome back! Now coming to the certification, who can actually take this exam, Scott? Scott: The MySQL HeatWave Implementation Associate Certification Exam is designed specifically for administrators and data scientists who want to provision, configure, and manage MySQL HeatWave for transactions, analytics, machine learning, and Lakehouse. 08:22 Nikita: Can someone who's just graduated, say an engineering graduate interested in data analytics, take this certification? Are there any prerequisites? What are the career prospects for them? Scott: There are no mandatory prerequisites, but anyone who wants to take the exam should have experience with MySQL HeatWave and other aspects of OCI, such as virtual cloud networks and identity and security processes. Also, the learning path on MyLearn will be extremely helpful when preparing for the exam, but you are not required to complete the learning path before registering for the exam. The exam focuses more on getting MySQL HeatWave running (and keeping it running) than accessing the data. That doesn't mean it is not helpful for someone interested in data analytics. I think it can be helpful for data analysts to understand how the system providing the data functions, even if it is at just a high level. It is also possible that data analysts might be responsible for setting up their own systems and importing and managing their own data. 09:23 Lois: And how do I get started if I want to get certified on MySQL HeatWave? Scott: So, you'll first need to go to mylearn.oracle.com and look for the “Become a MySQL HeatWave Implementation Associate” learning path. The learning path consists of over 10 hours of training across 8 different courses.  These courses include “Getting Started with MySQL HeatWave Database Service,” which offers an introduction to some Oracle Cloud functionality such as security and networking, as well as showing one way to connect to a MySQL HeatWave instance. Another course demonstrates how to configure MySQL instances and copy that configuration to other instances. Other courses cover how to migrate data into MySQL HeatWave, set up and manage high availability, and configure HeatWave for OLAP. You'll find labs where you can perform hands-on activities, student and activity guides, and skill checks to test yourself along the way. And there's also the option to Ask the Instructor if you have any questions you need answers to. You can also access the Oracle University Learning Community and discuss topics with others on the same journey. The learning path includes a practice exam to check your readiness to pass the certification exam. 10:33 Lois: Yeah, and remember, access to the entire learning path is free so there's nothing stopping you from getting started right away. Now Scott, what does the certification test you on? Scott: The MySQL HeatWave Implementation exam, which is an associate-level exam, covers various topics. It will validate your ability to identify key features and benefits of MySQL HeatWave and describe the MySQL HeatWave architecture; identify Virtual Cloud Network (VCN) requirements and the different methods of connecting to a MySQL HeatWave instance; manage the automatic backup process and restore database systems from these backups; configure and manage read replicas and inbound replication channels; import data into MySQL HeatWave; configure and manage high availability and clustering of MySQL HeatWave instances. I know this seems like a lot of different topics. That is why we recommend anyone interested in the exam follow the learning path. It will help make sure you have the exposure to all the topics that are covered by the exam. 11:35 Lois: Tell us more about the certification process itself. Scott: While the courses we already talked about are valuable when preparing for the exam, nothing is better than hands-on experience. We recommend that candidates have hands-on experience with MySQL HeatWave with real-world implementations. The format of the exam is Multiple Choice. It is 90 minutes long and consists of 65 questions. When you've taken the recommended training and feel ready to take the certification exam, you need to purchase the exam and register for it. You go through the section on things to do before the exam and the exam policies, and then all that's left to do is schedule the date and time of the exam according to when is convenient for you. 12:16 Nikita: And once you've finished the exam? Scott: When you're done your score will be displayed on the screen when you finish the exam. You will also receive an email indicating whether you passed or failed. You can view your exam results and full score report in Oracle CertView, Oracle's certification portal. From CertView, you can download and print your eCertificate and even share your newly earned badge on places like Facebook, Twitter, and LinkedIn. 12:38 Lois: And for how long does the certification remain valid, Scott? Scott: There is no expiration date for the exam, so the certification will remain valid for as long as the material that is covered remains relevant.  12:49 Nikita: What's the next step for me after I get this certification? What other training can I take? Scott: So, because this exam is an associate level exam, it is kind of a stepping stone along a person's MySQL training. I do not know if there are plans for a professional level exam for HeatWave, but Oracle University has several other training programs that are MySQL-specific. There are learning paths to help prepare for the MySQL Database Administrator and MySQL Database Developer exams. As with the HeatWave learning paths, the learning paths for these exams include video tutorials, hands-on activities, skill checks, and practice exams. 13:27 Lois: I think you've told us everything we need to know about this certification, Scott. Are there any parting words you might have? Scott: We know that the whole process of training and getting certified may seem daunting, but we've really tried to simplify things for you with the “Become a MySQL HeatWave Implementation Associate” learning path. It not only prepares you for the exam but also gives you experience with features of MySQL HeatWave that will surely be valuable in your career. 13:51 Lois: Thanks so much, Scott, for joining us today. Nikita: Yeah, we've had a great time with you. Scott: Thanks for having me. Lois: Next week, we'll get back to our focus on AI with a discussion on deep learning. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off. 14:07 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University  Podcast.

Oracle's next-gen cloud platform, Oracle Cloud Infrastructure, has been helping thousands of companies and millions of users run their entire application portfolio in the cloud. Today, the demand for OCI expertise is growing rapidly. Join Lois Houston and Nikita Abraham, along with Rohit Rahi, as they peel back the layers of OCI to discover why it is one of the world's fastest-growing cloud platforms.   Oracle MyLearn: https://mylearn.oracle.com/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X (formerly Twitter): https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, Kiran BR, Rashmi Panda, David Wright, the OU Podcast Team, and the OU Studio Team for helping us create this episode.   ------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started. 00:26 Lois: Welcome to the Oracle University Podcast. I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me today is Nikita Abraham, Principal Technical Editor. Nikita: Hi there! You're listening to our Best of 2023 series, where over the next few weeks, we'll be revisiting six of our most popular episodes of the year. 00:47 Lois: Today is episode 2 of 6, and we're throwing it back to our very first episode of the Oracle University Podcast. It was a conversation that Niki and I had with Rohit Rahi, Vice President, CSS OU Cloud Delivery. During this episode, we discussed Oracle Cloud Infrastructure's core coverage on different tiers. Nikita: But we began by asking Rohit to explain what OCI is and tell us about its key components. So, let's jump right in. 01:14 Rohit: Some of the world's largest enterprises are running their mission-critical workloads on Oracle's next generation cloud platform called Oracle Cloud Infrastructure. To keep things simple, let us break them down into seven major categories: Core Infrastructure, Database Services, Data and AI, Analytics, Governance and Administration, Developer Services, and Application Services.  But first, the foundation of any cloud platform is the global footprint of regions. We have many generally available regions in the world, along with multi-cloud support with Microsoft Azure and a differentiated hybrid offering called Dedicated Region Cloud@Customer.  01:57 Rohit: We have building blocks on top of this global footprint, the seven categories we just mentioned. At the very bottom, we have the core primitives: compute, storage, and networking. Compute services cover virtual machines, bare metal servers, containers, a managed Kubernetes service, and a managed VMWare service.  These services are primarily for performing calculations, executing logic, and running applications. Cloud storage includes disks attached to virtual machines, file storage, object storage, archive storage, and data migration services. 02:35 Lois: That's quite a wide range of storage services. So Rohit, we all know that networking plays an important role in connecting different services. These days, data is growing in size and complexity, and there is a huge demand for a scalable and secure approach to store data. In this context, can you tell us more about the services available in OCI that are related to networking, database, governance, and administration? 03:01 Rohit: Networking features let you set up software defined private networks in Oracle Cloud. OCI provides the broadest and deepest set of networking services with the highest reliability, most security features, and highest performance.  Then we have database services, we have multiple flavors of database services, both Oracle and open source. We are the only cloud that runs Autonomous Databases and multiple flavors of it, including OLTP, OLAP, and JSON.  And then you can run databases and virtual machines, bare metal servers, or even Exadata in the cloud. You can also run open source databases, such as MySQL and NoSQL in the Oracle Cloud Infrastructure.  03:45 Rohit: Data and AI Services, we have a managed Apache Spark service called Dataflow, a managed service for tracking data artifacts across OCI called Data Catalog, and a managed service for data ingestion and ETL called Data Integration.  We also have a managed data science platform for machine learning models and training. We also have a managed Apache Kafka service for event streaming use cases.  Then we have Governance and Administration services. These services include security, identity, and observability and management. We have unique features like compartments that make it operationally easier to manage large and complex environments. Security is integrated into every aspect of OCI, whether it's automatic detection or remediation, what we typically refer as Cloud Security Posture Management, robust network protection or encryption by default.  We have an integrated observability and management platform with features like logging, logging analytics, and Application Performance Management and much more.  04:55 Nikita: That's so fascinating, Rohit. And is there a service that OCI provides to ease the software development process? Rohit: We have a managed low code service called APEX, several other developer services, and a managed Terraform service called Resource Manager.  For analytics, we have a managed analytics service called Oracle Analytics Cloud that integrates with various third-party solutions.  Under Application services, we have a managed serverless offering, call functions, and API gateway and an Events Service to help you create microservices and event driven architectures.  05:35 Rohit: We have a comprehensive connected SaaS suite across your entire business, finance, human resources, supply chain, manufacturing, advertising, sales, customer service, and marketing all running on OCI.  That's a long list. And these seven categories and the services mentioned represent just a small fraction of more than 80 services currently available in OCI.  Fortunately, it is quick and easy to try out a new service using our industry-leading Free Tier account. We are the first cloud to offer a server for just a penny per core hour.  Whether you're starting with Oracle Cloud Infrastructure or migrating your entire data set into it, we can support you in your journey to the cloud.   06:28 Have an idea and want a platform to share your technical expertise? Head over to the new Oracle University Learning Community. Drive intellectual, free-flowing conversations with your peers. Listen to experts and learn new skills. If you are already an Oracle MyLearn user, go to MyLearn to join the Community. You will need to log in first. If you have not yet accessed Oracle MyLearn, visit mylearn.oracle.com and create an account to get started.  Join the conversation today! 07:04 Nikita: Welcome back! Now let's listen to Rohit explain the core constructs of OCI's physical architecture, starting with regions. Rohit: Region is a localized geographic area comprising of one or more availability domains.  Availability domains are one or more fault tolerant data centers located within a region, but connected to each other by a low latency, high bandwidth network. Fault domains is a grouping of hardware and infrastructure within an availability domain to provide anti-affinity. So think about these as logical data centers.  Today OCI has a massive geographic footprint around the world with multiple regions across the world. And we also have a multi-cloud partnership with Microsoft Azure. And we have a differentiated hybrid cloud offering called Dedicated Region Cloud@Customer.  08:02 Lois: But before we dive into the physical architecture, can you tell us…how does one actually choose a region?  Rohit: Choosing a region, you choose a region closest to your users for lowest latency and highest performance. So that's a key criteria. The second key criteria is data residency and compliance requirements. Many countries have strict data residency requirements, and you have to comply to them. And so you choose a region based on these compliance requirements.  08:31 Rohit: The third key criteria is service availability. New cloud services are made available based on regional demand at times, regulatory compliance reasons, and resource availability, and several other factors. Keep these three criteria in mind when choosing a region.  So let's look at each of these in a little bit more detail. Availability domain. Availability domains are isolated from each other, fault tolerant, and very unlikely to fail simultaneously. Because availability domains do not share physical infrastructure, such as power or cooling or the internal network, a failure that impacts one availability domain is unlikely to impact the availability of others.  A particular region has three availability domains. One availability domain has some kind of an outage, is not available. But the other two availability domains are still up and running.  09:26 Rohit: We talked about fault domains a little bit earlier. What are fault domains? Think about each availability domain has three fault domains. So think about fault domains as logical data centers within availability domain.  We have three availability domains, and each of them has three fault domains. So the idea is you put the resources in different fault domains, and they don't share a single point of hardware failure, like physical servers, physical rack, top of rack switches, a power distribution unit. You can get high availability by leveraging fault domains.  We also leverage fault domains for our own services. So in any region, resources in at most one fault domain are being actively changed at any point in time. This means that availability problems caused by change procedures are isolated at the fault domain level. And moreover, you can control the placement of your compute or database instances to fault domain at instance launch time. So you can specify which fault domain you want to use.  10:29 Nikita: So then, what's the general guidance for OCI users?  Rohit: The general guidance is we have these constructs, like fault domains and availability domains to help you avoid single points of failure. We do that on our own. So we make sure that the servers, the top of rack switch, all are redundant. So you don't have hardware failures or we try to minimize those hardware failures as much as possible. You need to do the same when you are designing your own architecture.  So let's look at an example. You have a region. You have an availability domain. And as we said, one AD has three fault domains, so you see those fault domains here.  11:08 Rohit: So first thing you do is when you create an application you create this software-defined virtual network. And then let's say it's a very simple application. You have an application tier. You have a database tier.  So first thing you could do is you could run multiple copies of your application. So you have an application tier which is replicated across fault domains. And then you have a database, which is also replicated across fault domains.  11:34 Lois: What's the benefit of this replication, Rohit?  Rohit: Well, it gives you that extra layer of redundancy. So something happens to a fault domain, your application is still up and running.  Now, to take it to the next step, you could replicate the same design in another availability domain. So you could have two copies of your application running. And you can have two copies of your database running.  11:57 Now, one thing which will come up is how do you make sure your data is synchronized between these copies? And so you could use various technologies like Oracle Data Guard to make sure that your primary and standby-- the data is kept in sync here. And so that-- you can design your application-- your architectures like these to avoid single points of failure. Even for regions where we have a single availability domain, you could still leverage fault domain construct to achieve high availability and avoid single points of failure.  12:31 Nikita: Thank you, Rohit, for taking us through OCI at a high level.  Lois: For a more detailed explanation of OCI, please visit mylearn.oracle.com, create a profile if you don't already have one, and get started on our free training on OCI Foundations.  Nikita: We hope you enjoyed that conversation. Join us next week for another throwback episode. Until then, this is Nikita Abraham... Lois: And Lois Houston, signing off! 12:57 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Follow: https://stree.ai/podcast | Sub: https://stree.ai/sub | New episodes every Monday! Join host Tim as he talks with Sandeep Dabade through demystifying the impressive star-tree index of Apache Pinot. Discover how this advanced feature optimizes OLAP databases, striking a balance between storage and high-speed query performance, and listen to real-world test cases showcasing its lightning-fast capabilities. Sandeep's blogs:► https://startree.ai/blog/best-practices-for-designing-tables-in-apache-pinot► https://startree.ai/blog/star-tree-indexes-in-apache-pinot-part-1-understanding-the-impact-on-query-performance► https://startree.ai/blog/star-tree-indexes-in-apache-pinot-part-2-understanding-the-impact-during-high-concurrency► https://startree.ai/blog/star-tree-index-in-apache-pinot-part-3-understanding-the-impact-in-real-customer

Summary Databases are the core of most applications, whether transactional or analytical. In recent years the selection of database products has exploded, making the critical decision of which engine(s) to use even more difficult. In this episode Tanya Bragin shares her experiences as a product manager for two major vendors and the lessons that she has learned about how teams should approach the process of tool selection. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It's the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it's real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold (https://www.dataengineeringpodcast.com/datafold) Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at dataengineeringpodcast.com/miro (https://www.dataengineeringpodcast.com/miro). That's three free boards at dataengineeringpodcast.com/miro (https://www.dataengineeringpodcast.com/miro). Your host is Tobias Macey and today I'm interviewing Tanya Bragin about her views on the database products market Interview Introduction How did you get involved in the area of data management? What are the aspects of the database market that keep you interested as a VP of product? How have your experiences at Elastic informed your current work at Clickhouse? What are the main product categories for databases today? What are the industry trends that have the most impact on the development and growth of different product categories? Which categories do you see growing the fastest? When a team is selecting a database technology for a given task, what are the types of questions that they should be asking? Transactional engines like Postgres, SQL Server, Oracle, etc. were long used as analytical databases as well. What is driving the broad adoption of columnar stores as a separate environment from transactional systems? What are the inefficiencies/complexities that this introduces? How can the database engine used for analytical systems work more closely with the transactional systems? When building analytical systems there are numerous moving parts with intricate dependencies. What is the role of the database in simplifying observability of these applications? What are the most interesting, innovative, or unexpected ways that you have seen Clickhouse used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on database products? What are your prodictions for the future of the database market? Contact Info LinkedIn (https://www.linkedin.com/in/tbragin/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Clickhouse (https://clickhouse.com/) Podcast Episode (https://www.dataengineeringpodcast.com/clickhouse-data-warehouse-episode-88/) Elastic (https://www.elastic.co/) OLAP (https://en.wikipedia.org/wiki/Online_analytical_processing) OLTP (https://en.wikipedia.org/wiki/Online_transaction_processing) Graph Database (https://en.wikipedia.org/wiki/Graph_database) Vector Database (https://en.wikipedia.org/wiki/Vector_database) Trino (https://trino.io/) Presto (https://prestodb.io/) Foreign data wrapper (https://wiki.postgresql.org/wiki/Foreign_data_wrappers) dbt (https://www.getdbt.com/) Podcast Episode (https://www.dataengineeringpodcast.com/dbt-data-analytics-episode-81/) OpenTelemetry (https://opentelemetry.io/) Iceberg (https://iceberg.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/tabular-iceberg-lakehouse-tables-episode-363) Parquet (https://parquet.apache.org/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

No episódio de hoje, Luan Moreno, Mateus Oliveira e Orlando Marley entrevistam Bill Inmon, criador do conceito de Data Warehouse e escritor de diversos livros com temáticas voltadas para dados.Data Warehouse é o conceito de centralização de dados analíticos das organizações, de forma estruturar um visão 360° do business. Neste episódio, você irá aprender: Diferenças entre OLTP e OLAP;Histórico dos dados para tomada de decisão;Criar um processo resiliente para entender os fatos dos dados.Falamos também, neste bate-papo, sobre os seguintes temas: História do Bill Inmon;Pilares de sistemas analíticos;Nova geração de plataforma de dados analíticos;Aprenda mais sobre análise de dados, como utilizar tecnologias para tornar o seu ambiente analítico confiável e resiliente com as palavras do pai do Data Warehouse. Bill Inmon = Linkedin Luan Moreno = https://www.linkedin.com/in/luanmoreno/

Episode #29 of "Can I get that software in blue?", a podcast by and for people engaged in technology sales. If you are in the technology presales, solution architecture, sales, support or professional services career paths then this show is for you! Tanya Bragin is the VP of Product at ClickHouse, a very fast open source OLAP databases used by some of the biggest companies in the world. Tanya was hired to lead the effort to build a SaaS offering of ClickHouse, having previously served in similar roles as a VP of Product at Elastic. Come listen to Tanya's unique insights about how to take an open source project to market with a SaaS offering including how to engage and motivate the engineering team, deciding which features to build, acquire, or buy, how to hire and set goals for IC PMs, and most importantly HOW TO SET PRICING. Our website: https://softwareinblue.com Twitter: https://twitter.com/softwareinblue LinkedIn: https://www.linkedin.com/showcase/softwareinblue Make sure to subscribe or follow us to get notified about our upcoming episodes: Youtube: https://www.youtube.com/channel/UC8qfPUKO_rPmtvuB4nV87rg Apple Podcasts: https://podcasts.apple.com/us/podcast/can-i-get-that-software-in-blue/id1561899125 Spotify: https://open.spotify.com/show/25r9ckggqIv6rGU8ca0WP2 Links mentioned in the episode: https://clickhouse.com/blog/my-journey-as-a-serial-startup-product-manager https://clickhouse.com/blog/vector-search-clickhouse-p1 https://clickhouse.com/blog/vector-search-clickhouse-p2

In modern systems, the amount of data keeps getting larger, and the time available keeps getting shorter. So it's almost inevitable that we're augmenting our general-purpose databases with dedicated analytics databases.This week we dive into the world of OLAP with a thorough look at Clickhouse, a high-performance, columnar database designed to "query billions of rows in microseconds."Alasdair Brown joins us to discuss what Clickhouse is, how it performs queries so quickly, and where it fits into a wider system. We talk about its origins as a Google Analytics-like, and how it's grown into one of the most popular OLAP databases around.There's a lot of ground to cover, and a lot of questions to ask, all in the service of faster answers...--Alasdair's Blog: alasdairb.comAlasdair on Threads: https://www.threads.net/@sdairsabAlasdair on LinkedIn https://www.linkedin.com/in/alasdair-brownKris on Twitter: https://twitter.com/krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Clickhouse: https://clickhouse.com/Tinybird: https://www.tinybird.co/Birdhouse in your Soul: https://youtu.be/vn_or9gEB6g

"Designing Data-Intensive Applications"、通称 ”DDIA" 本の Ch3&4 を読んで感想を語りました。 ⁠⁠Amazon.co.jp (英語版)⁠⁠ ⁠⁠Amazon.co.jp (日本語版)⁠⁠ ⁠⁠Designing Data-Intensive Applications⁠⁠ Database index Log-structured merge-tree (LSM-tree) B-tree protocolbuffers/protobuf msgpack.org Apache Avro Apache Thrift Online transaction processing (OLTP) Online analytical processing (OLAP)

On this episode of Data Driven, Frank and Andy interview Adam Ross Nelson. Adam is a consultant, where he provides insights on data science, machine learning and data governance. He recently wrote a book to help people get started in data science careers. Get the bookHow to Become a Data Scientist: A Guide for Established ProfessionalsSpeaker BioAdam Ross Nelson is an individual who initially pursued a career in law but ended up making a transition into education. After attending law school and working in administrative and policy roles in colleges and universities for several years, Adam hit a plateau in his career. Despite being a runner-up in national job searches multiple times, he felt that his lack of a PhD hindered his advancement in academia, while his legal background prevented him from being taken seriously by law professionals. Consequently, Adam decided to pursue a PhD in order to overcome this hurdle. During his PhD program, Adam discovered his passion and knack for statistics. His focus shifted towards predictive analytics projects, specifically ones related to identifying students in need of academic support. As he shared his work with friends, family, and coworkers, they began referring to him as a data scientist, a label that Adam initially resisted due to his legal and educational background. However, he eventually embraced the moniker, and even his boss started referring to him as the office's data scientist, despite HR not recognizing the title.Show Notes[00:03:26] Transitioning from law to education administration, plateaued career, runner-up in job searches, pursued PhD, became data scientist.[00:08:58] Data seen as liability, now asset. Examples: DBA's OLAP analysis, Walmart's weather-based inventory management.[00:12:56] Dotcom crash aftermath: fierce competition for jobs.[00:22:48] Salespeople have deep-seated insecurities and unique perspective.[00:29:31] Various classifications of data scientists and career advice.[00:35:55] "No full-field midfielder, data science is teamwork"[00:39:23] Navigating job descriptions for transitioning professionals.[00:42:56] Career coach helps professionals transition into data science.[00:49:41] First job: English teacher in Budapest, Hungary. Second job: Speaker for Mothers Against Drunk Driving.[00:56:30] Concerns about reliance on technology, especially AI.[01:00:22] Food options in lobbying are better in DC & state capitals. Also, check out the funny WY Files YouTube channel.[01:04:21] You can't separate them: LLM, bias, internet.[01:10:23] Ethics in consulting and avoiding dilemmas.

No episódio de hoje, Luan Moreno e Mateus Oliveira entrevistam Neha Pawar, atualmente Founder Engineer na StarTree.Apache Pinot é um banco de dados OLAP de baixa latência, que foi desenvolvido para queries analíticas dentro do Linkedin.O objetivo é resolver um dos problemas que tecnologias como o Apache Kafka não resolvem, consultar bilhões de eventos com performance e baixa latêcia .  Com  Apache Pinot, você tem os seguintes benefícios: Alto desempenho de consultas analíticas;  Dados que residem no Apache Pinot são comprimidos; Habilita milhares de acessos concorrentes aos dados residentes no Apache Pinot.Falamos também sobre os temas: Criação do Apache Pinot; User Facing Analytics;Tipos de Deployment no Apache Pinot;  O que vem por aí no Apache Pinot.Aprenda mais sobre Apache Pinot, uma tecnologia capaz de armazenar dados em tempo real, e executar queries com baixa latência, chegando até milissegundos.Neha Pawar = Linkedinhttps://pinot.apache.org/ Luan Moreno = https://www.linkedin.com/in/luanmoreno/

María Gordillo Villa, mexicana de Guadalajara, después de vivir toda una vida, con su familia, en el estado de Colorado, como inmigrantes ilegales. En el 2019, se unió al Centro legal para los inmigrantes del condado de Boulder, se convirtió en directora de Participación Comunitaria y actualmente está estudiando para recibir su acreditación de OLAP. María trabaja directamente con los beneficiarios de DACA proporcionándoles asistencia legal y los informa y guía en todo tipo de cambios legales que se presentan. Aparte, dedica todo su tiempo en brindar apoyo a todos los inmigrantes indocumentados para que legalicen su situación en Estados Unidos.María recuerda: “Muchas veces me toco tener debates muy fuertes con gente mayor de la raza blanca, tratar de que ellos se educaran de lo que es la migración. Cuando apliqué a la universidad, un consejero me dijo que no iba a llegar lejos porque no tenía un número de seguro social, para mí eso fue algo que quebró mi sueño.

In this episode, Lois Houston and Nikita Abraham are joined by Autumn Black to discuss MySQL Database, a fully-managed database service powered by the integrated HeatWave in-memory query accelerator.   Oracle MyLearn: https://mylearn.oracle.com/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ Twitter: https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Deepak Modi, Ranbir Singh, and the OU Studio Team for helping us create this episode.   ---------------------------------------------------------   Episode Transcript: 00;00;00;00 - 00;00;39;08 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started. Hello and welcome to the Oracle University Podcast. You're listening to our second season Oracle Database Made Easy. I'm Lois Houston, Director of Product Innovation and Go to Market Programs with Oracle University.   00;00;39;10 - 00;01;08;03 And with me is Nikita Abraham, Principal Technical Editor. Hi, everyone. In our last episode, we had a really fascinating conversation about Oracle Machine Learning with Cloud Engineer Nick Commisso. Do remember to catch that episode if you missed it. Today, we have with us Autumn Black, who's an Oracle Database Specialist. Autumn is going to take us through MySQL, the free version and the Enterprise Edition, and MySQL Data Service.   00;01;08;05 - 00;01;39;16 We're also going to ask her about HeatWave. So let's get started. Hi, Autumn. So tell me, why is MySQL such a popular choice for developers? MySQL is the number one open-source database and the second most popular database overall after the Oracle Database. According to a Stack Overflow survey, MySQL has been for a long time and remains the number one choice for developers, primarily because of its ease of use, reliability, and performance.   00;01;39;17 - 00;02;08;22 And it's also big with companies? MySQL is used by the world's most innovative companies. This includes Twitter, Facebook, Netflix, and Uber. It is also used by students and small companies. There are different versions of MySQL, right? What are the main differences between them when it comes to security, data recovery, and support? MySQL comes in two flavors: free version or paid version.   00;02;08;24 - 00;02;45;05 MySQL Community, the free version, contains the basic components for handling data storage. Just download it, install it, and you're ready to go. But remember, free has costs. That stored data is not exactly secure and data recovery is not easy and sometimes impossible. And there is no such thing as free MySQL Community support. This is why MySQL Enterprise Edition was created, to provide all of those missing important pieces: high availability, security, and Oracle support from the people who build MySQL.   00;02;45;10 - 00;03;09;24 You said MySQL is open source and can be easily downloaded and run. Does it run on-premises or in the cloud? MySQL runs on a local computer, company's data center, or in the cloud. Autumn, can we talk more about MySQL in the cloud? Today, MySQL can be found in Amazon RDS and Aurora, Google Cloud SQL, and Microsoft Azure Database for MySQL.   00;03;09;27 - 00;03;35;23 They all offer a cloud-managed version of MySQL Community Edition with all of its limitations. These MySQL cloud services are expensive and it's not easy to move data away from their cloud. And most important of all, they do not include the MySQL Enterprise Edition advanced features and tools. And they are not supported by the Oracle MySQL experts.   00;03;35;25 - 00;04;07;03 So why is MySQL Database Service in Oracle Cloud Infrastructure better than other MySQL cloud offerings? How does it help data admins and developers? MySQL Database Service in Oracle Cloud Infrastructure is the only MySQL database service built on MySQL Enterprise Edition and 100% built, managed, and supported by the MySQL team. Let's focus on the three major categories that make MySQL Database Service better than the other MySQL cloud offerings: ease of use, security, and enterprise readiness.   00;04;07;03 - 00;04;44;24 MySQL DBAs tend to be overloaded with mundane database administration tasks. They're responsible for many databases, their performance, security, availability, and more. It is difficult for them to focus on innovation and on addressing the demands of lines of business. MySQL is fully managed on OCI. MySQL Database Service automates all those time-consuming tasks so they can improve productivity and focus on higher value tasks.   00;04;44;26 - 00;05;07;13 Developers can quickly get all the latest features directly from the MySQL team to deliver new modern apps. They don't get that on other clouds that rely on outdated or forked versions of MySQL. Developers can use the MySQL Document Store to mix and match SQL and NoSQL content in the same database as well as the same application.   00;05;07;19 - 00;05;30;26 Yes. And we're going to talk about MySQL Document Store in a lot more detail in two weeks, so don't forget to tune in to that episode. Coming back to this, you spoke about how MySQL Database Service or MDS on OCI is easy to use. What about its security? MDS security first means it is built on Gen 2 cloud infrastructure.   00;05;30;28 - 00;05;57;13 Data is encrypted for privacy. Data is on OCI block volume. So what does this Gen 2 cloud infrastructure offer? Is it more secure? Oracle Cloud is secure by design and architected very differently from the Gen 1 clouds of our competitors. Gen 2 provides maximum isolation and protection. That means Oracle cannot see customer data and users cannot access our cloud control computer.   00;05;57;15 - 00;06;27;09 Gen 2 architecture allows us to offer superior performance on our compute objects. Finally, Oracle Cloud is open. Customers can run Oracle software, third-party options, open source, whatever you choose without modifications, trade-offs, or lock-ins. Just to dive a little deeper into this, what kind of security features does MySQL Database Service offer to protect data? Data security has become a top priority for all organizations.   00;06;27;12 - 00;06;55;17 MySQL Database Service can help you protect your data against external attacks, as well as internal malicious users with a range of advanced security features. Those advanced security features can also help you meet industry and regulatory compliance requirements, including GDPR, PCI, and HIPPA. When a security vulnerability is discovered, you'll get the fix directly from the MySQL team, from the team that actually develops MySQL.   00;06;55;19 - 00;07;22;16 I want to talk about MySQL Enterprise Edition that you brought up earlier. Can you tell us a little more about it? MySQL Database Service is the only public cloud service built on MySQL Enterprise Edition, which includes 24/7 support from the team that actually builds MySQL, at no additional cost. All of the other cloud vendors are using the Community Edition of MySQL, so they lack the Enterprise Edition features and tools.   00;07;22;22 - 00;07;53;24 What are some of the default features that are available in MySQL Database Service? MySQL Enterprise scalability, also known as the thread pool plugin, data-at-rest encryption, native backup, and OCI built-in native monitoring. You can also install MySQL Enterprise Monitor to monitor MySQL Database Service remotely. MySQL works well with your existing Oracle investments like Oracle Data Integrator, Oracle Analytics Cloud, Oracle GoldenGate, and more.   00;07;53;27 - 00;08;17;20 MySQL Database Service customers can easily use Docker and Kubernetes for DevOps operations. So how much of this is managed by the MySQL team and how much is the responsibility of the user? MySQL Database Service is a fully managed database service. A MySQL Database Service user is responsible for logical schema modeling, query design and optimization, define data access and retention policies.   00;08;17;22 - 00;08;44;26 The MySQL team is responsible for providing automation for operating system installation, database and OS patching, including security patches, backup, and recovery. The system backs up the data for you, but in an emergency, you can restore it to a new instance with a click. Monitoring and log handling. Security with advanced options available in MySQL Enterprise Edition.   00;08;44;28 - 00;09;01;18 And of course, maintaining the data center for you. To use MDS, users must have OCI tenancy, a compartment, belong to a group with required policies.   00;09;01;21 - 00;09;28;28 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You'll find training on everything from cloud computing, database, and security to artificial intelligence and machine learning, all of which is available free to subscribers. So get going. Pick a course of your choice, get certified, join the Oracle University Learning Community, and network with your peers. If you're already an Oracle MyLearn user, go to MyLearn to begin your journey.   00;09;29;03 - 00;09;40;24 If you have not yet accessed Oracle MyLearn, visit mylearn.oracle.com and create an account to get started.   00;09;40;27 - 00;10;05;20 Welcome back! Autumn, tell us about the system architecture of MySQL Database Service. A database system is a logical container for the MySQL instance. It provides an interface enabling management of tasks, such as provisioning, backup and restore, monitoring, and so on. It also provides a read and write endpoint, enabling you to connect to the MySQL instance using the standard protocols.   00;10;05;28 - 00;10;31;27 And what components does a MySQL Database Service DB system consist of? A computer instance, an Oracle Linux operating system, the latest version of MySQL server Enterprise Edition, a virtual network interface card, VNIC, that attaches the DB system to a subnet of the virtual cloud network, network-attached higher performance block storage. Is there a way to monitor how the MySQL Database Service is performing?   00;10;31;29 - 00;10;59;29 You can monitor the health, capacity, and performance of your Oracle Cloud Infrastructure MySQL Database Service resources by using metrics, alarms, and notifications. The MySQL Database Service metrics enable you to measure useful quantitative data about your MySQL databases such as current connection information, statement activity, and latency, host CPU, memory, and disk I/O utilization, and so on.   00;11;00;03 - 00;11;23;15 You can use metrics data to diagnose and troubleshoot problems with MySQL databases. What should I keep in mind about managing the SQL database? Stopped MySQL Database Service system stops billing for OCPUs, but you also cannot connect to the DB system. During MDS automatic update, the operating system is upgraded along with patching of the MySQL server.   00;11;23;17 - 00;11;49;15 Metrics are used to measure useful data about MySQL Database Service system. Turning on automatic backups is an update to MDS to enable automatic backups. MDS backups can be removed by using the details pages and OCI and clicking Delete. Thanks for that detailed explanation on MySQL, Autumn. Can you also touch upon MySQL HeatWave? Why would you use it over traditional methods of running analytics on MySQL data?   00;11;49;18 - 00;12;18;01 Many organizations choose MySQL to store their valuable enterprise data. MySQL is optimized for Online Transaction Processing, OLTP, but it is not designed for Online Analytic Processing, OLAP. As a result, organizations that need to efficiently run analytics on data stored in MySQL database move their data to another database to run analytic applications such as Amazon Redshift.   00;12;18;04 - 00;12;41;22 MySQL HeatWave is designed to enable customers to run analytics on data that is stored in MySQL database without moving data to another database. What are the key features and components of HeatWave? HeatWave is built on an innovative in-memory analytics engine that is architected for scalability and performance, and is optimized for Oracle Cloud Infrastructure, OCI.   00;12;41;24 - 00;13;05;29 It is enabled when you add a HeatWave cluster to a MySQL database system. A HeatWave cluster comprises a MySQL DB system node and two or more HeatWave nodes. The MySQL DB system node includes a plugin that is responsible for cluster management, loading data into the HeatWave cluster, query scheduling, and returning query results to the MySQL database system.   00;13;06;02 - 00;13;29;15 The HeatWave nodes store data and memory and processed analytics queries. Each HeatWave node contains an instance of the HeatWave. The number of HeatWave nodes required depends on the size of your data and the amount of compression that is achieved when loading the data into the HeatWave cluster. Various aspects of HeatWave use machine-learning-driven automation that helps to reduce database administrative costs.   00;13;29;18 - 00;13;52;11 Thanks, Autumn, for joining us today. We're looking forward to having you again next week to talk to us about Oracle NoSQL Database Cloud Service. To learn more about MySQL Data Service, head over to mylearn.oracle.com and look for the Oracle Cloud Data Management Foundations Workshop. Until next time, this is Nikita Abraham and Lois Houston signing off.   00;13;52;14 - 00;16;33;05 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

This interview was recorded for GOTO Unscripted.gotopia.techRead the full transcription of this interview hereTim Berglund - VP DevRel at StarTree & Author of "Gradle Beyond the Basics"Adi Polak - VP of Developer Experience at Treeverse & Contributing to lakeFS OSSRESOURCESTimtimberglund.comtwitter.com/tlberglundlinkedin.com/in/tlberglundAditwitter.com/AdiPolakinstagram.com/polak.codelinkedin.com/in/polak-adiTools & companiespinot.apache.orgtwitter.com/startreedatalinkedin.com/company/startreedatadev.startree.aistree.ai/slackYT videosData Mesh • Zhamak DehghaniBeyond Microservices • Gwen ShapiraDESCRIPTIONAdi Polak and Tim Berglund explore the concept of analytics and what it truly means in the software development world. They delve into the benefits of real-time analytics for product development, highlighting the fine line between compute and storage and the technical requirements for achieving effective real-time analytics. They also discuss the applications of real-time analytics through the lens of Apache Pinot and StarTree Cloud, exploring use cases such as the popular "Who's Watched My Profile on LinkedIn" feature powered by Apache Pinot.RECOMMENDED BOOKSAdi Polak • Scaling Machine Learning with SparkTim Berglund • Gradle Beyond the BasicsTim Berglund & Matthew McCullough • Building and Testing with GradleMark Needham • Building Real-Time Analytics SystemsGwen Shapira, Todd Palino, Rajini Sivaram & Krit Petty • Kafka: The Definitive GuideTwitterLinkedInFacebookLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted almost daily

From OLAP to Vertipaq to Power BI, Microsoft has a rich history of innovation and evolution in business intelligence. As data becomes an ever-increasing priority for organizations around the globe, Microsoft is now focused on the future with the launch of Microsoft Fabric, a unified SaaS solution which integrates all your data in one place. Fabric makes data management easier and more accessible for every user who works with data. In this episode you'll learn: How Amir and his team first pitched the idea of what is now Microsoft OLAP Services What happens when you think about the unevenness of data as a design principle Why you should demo products with things, people have opinions about Some questions we ask: How did the transition from OLAP to Vertipaq happen? When is the next great thing coming from Microsoft? What is Microsoft Fabric, and what will it do for the world of data? Guest bio Amir Netz, CTO of Microsoft's Intelligence Platform, including Power BI, Synapse, and more, joins Patrick LeBlanc on this week's episode of Insights Tomorrow. Amir is one of the leading world experts in business intelligence and analytics, holding over 80 patents. He is also the chief architect of Microsoft's BI offerings, including Power BI, Azure Synapse, Azure Data Factory, and more. Resources: View Amir Netz on LinkedIn View Patrick LeBlanc on LinkedIn Discover and follow other Microsoft podcasts at microsoft.com/podcasts Hosted on Acast. See acast.com/privacy for more information.

No episódio de hoje, Luan Moreno e Mateus Oliveira entrevistaram Xiran Waibel, atualmente engenheira de dados Sênior na Netflix.A Engenharia de Dados é um das profissões que estão em alta no mercado de trabalho, mas entender como funciona é algo que até hoje as empresas tem dificuldades.Engenharia de Dados engloba:Entendimento de novas tecnologias orientadas a Big DataTrabalhar com soluções de dados que resolvem problemas de negócioConstruções de pipelines de dados resilientes e escaláveisFalamos também nesse bate-papo sobre os seguintes temas:Engenharia de Dados na Netflix;Dicas de Engenharia de Dados;Soft Skills;Comunidade.Aprenda um pouco como a Netflix trabalha utilizando dados como um dos produtos mais valiosos da empresa, além de uma cultura interna forte e funcional.Xiran Waibel Medium  Luan Moreno = https://www.linkedin.com/in/luanmoreno/

这一期可谓众望所归，Monica 硬核对话 PingCAP 联合创始人CTO 黄东旭。大年初四，我们在广西老家的一个露营地，吹着15度的暖风，聊了两个小时的数据库、中美开发者市场和技术产品哲学，好不惬意！ Hello World, who is OnBoard?! 如果你对开源有所关注，一定知道开源分布式数据库 TiDB 及其背后的公司 PingCAP。PingCAP 可以说是中国商业化开源公司的先驱。2015年成立至今，TiDB 从零开始，在Github 上超过3万star, 超过800位来自世界各地的贡献者，除了包括众多一线互联网大厂在内的开源用户，PingCAP 还服务了20多个国家3000多个客户。 作为联合创始人和 CTO 的黄东旭，不仅是资深的基础软件工程师，架构师，还是狂热的开源爱好者以及开源软件作者，内存数据库 Redis 的高性能集群架构解决方案 Codis 就是他的作品之一 。 此外，PingCAP 也可谓是中国基础软件公司走向世界的先行者。过去一年多的时间，东旭几乎全身心铺在硅谷，对于中美市场异同、什么是给开发者用的数据库，什么是未来的开发范式，当然还有开脑洞的讨论：现代AI发展会对数据领域有什么影响？这次掏心窝的分享，一定能给你非常多启发！ 最后需要一提的是，生活中的东旭还是一名摇滚乐手。本期最后的彩蛋，你会听到他展示最新学习的乐器！ 干货满满准备上车，Enjoy! 【感谢AroundDeal 赞助本期播客！】 随着越来越多 IT SaaS、智能制造企业都开始开拓全球市场，精准获取海外B端客户线索就成了首要问题。AroundDeal 为企业提供全球商业信息SaaS平台。他们的平台上1亿多条联系人、企业及商业情报信息，覆盖全球200多个国家地区，3000多种细分行业，并且持续更新。绝对是企业出海的必备神器！ OnBoard! 听众还有福利！访问 AroundDeal.com，在 Contact Sales 中备注 Onboard, 即可领取七天免费试用！还不赶紧去试试，立即找到你的下一个海外理想客户！ 我们都聊了什么 02:30 开场：PingCAP 介绍，发展历程的几个重要节点 07:00 OLAP, OLTP 科普：场景和开发难点有什么不同 12:12 “未来的数据库都会是 HTAP 数据库”？！为什么说 HTAP 的核心能力是在TP能力 21:35 分久必合，数据库长尾需求会越来越收敛吗 24:03 Why now: 为什么 HTAP 概念在最近几年开始被广泛接受？ 27:19 为什么 HTAP 概念在 infra 成熟的美国，流行得反而更晚？ 29:55 主流的 HTAP 架构是怎样的？用户应该如何选型？ 34:26 各个大厂都在跟进 HTAP 产品，对于早期公司意味着什么？ 37:11 如何理解“万物皆可 SQL”？ 对于数据库厂商意味着什么？ 41:17 什么是“数据库的第一性原理”？ 46:25 Vercel 如何做好开发者体验？为什么说要做好 infra, 你应该关注的反而不是 infra? 53:23 对于新创的数据库公司，没有 Serverless 就上不了牌桌？ 59:45 serverless 开脑洞的未来！解决数据孤岛的终极方案 64:38 好的数据库 vs 好的数据库产品 66:12 为什么说新的数据库公司，需要新的研发组织？ PingCAP 发生了哪些组织挑战与变革？ 71:18 数据库用户的组织架构在发生哪些变化？ 74:43 开源社区在组织不同阶段的作用有什么不一样？为什么说期待开源到商业转化不能太乐观？ 80:27 在美国有哪些新出现的 infra 公司和新的技术趋势？ 84:46 开拓北美市场，要从科技行业客户破圈，有哪些挑战？对搭建团队有哪些挑战？ 92:21 美国之外的海外市场：东南亚有惊喜，顺序打法有讲究 98:01 中国与海外市场的异同，为什么创业公司也要先啃硬骨头 100:11 创业8年回顾：有哪些经验和心得？ 104:11 未来令人兴奋的机会 108:40 不得不了的：chatGPT, 生成式 AI 的脑洞 111:58 快问快答：有彩蛋！ 我们提到的公司 & 重点名词 Snowflake SingleStore Neon Vercel Supabase OSS Insight Snowflake Unistore Google Spanner ZeroETL Reverse ETL OLAP OLTP Serverless 嘉宾的推荐 推荐的书：禅与摩托车维修艺术（Zen and the Art of Motorcycle Maintenance, by Robert M.Pirsig） 推荐的书：Unix 编程艺术, by Eric S·Raymond Rob Pike, Go 语言之父 Werner Vogels, Amazon CTO Bansuri, 印度乐器 喜欢的音乐人：Sonic Youth 欢迎关注M小姐的微信公众号，了解更多中美软件行业的干货内容！ M小姐研习录 (ID: MissMStudy) 大家的点赞、评论、转发是对我们最好的鼓励！ 如果你有希望我们聊的话题，希望我们邀请的访谈嘉宾，都欢迎在留言中告诉我们 ❤️

В этом выпуске: как упростить себе жизнь при помощи Хренолоджи, специализированный индексы для OLAP не имеющие ничего общего с BRIN, новый проект Wildebeest от Cloudflare, а также забытые игры про обтягивающий водолазный костюм и гигантского осьминога с щупальцами, удачно незалоченный от перепрошивки Nintendo Game & Watch, а также темы наших слушателей. Шоуноты: [00:00:24] Чему мы… Читать далее →

On today's episode, we're joined by David Wang, VP of Product Marketing at Imply and Muthu Lalapet, Director of Worldwide Sales Engineering at Imply to dig into Apache Druid, a high performance, real-time analytics database. Thousands of companies are already using Druid today, from Netflix to Salesforce. But what is Apache Druid best used for? What types of projects? What data sets are Druid users working with? What are companies doing with Druid?  Listen to hear real-life examples of where Druid works best: Operational visibility at scale, customer-facing analytics, rapid-drill down exploration and real-time decisioning.

Neste episódio entrevistamos o Kishore Gopalakrishna, Co-Fundador e CEO da empresa StarTree, Luan Moreno e Mateus Oliveira batem um papo com o co-criador dessa poderosa ferramenta chamada Apache Pinot.O Pinot é um OLAP DataStore desenvolvido para responder consultas analíticas com tempo de resposta na casa dos milissegundos, podendo ser considerado um banco de dados para consultas em tempo-real. Capaz de ingerir de fontes de dados em Batch (Hadoop HDFS, Amazon S3, Azure ADLS, Google Cloud Storage), bem como fontes de dados em Stream (Apache Kafka, Apache Pulsar, Amazon Kinesis).O Pinot foi projetado para executar consultas OLAP em tempo real, com baixa latência em grandes quantidades de eventos para entregar o conceito de User-Facing Analytics.Foi criado e desenvolvido por engenheiros do LinkedIn e do Uber e projetado para escalar e expandir sem limites.Apache PinotKishore GopalakrishnaStarTree Luan Moreno = https://www.linkedin.com/in/luanmoreno/

Datengetriebene Entscheidungen oder auch "Glaube keiner Statistik, die du nicht selbst gefälscht hast".Entscheidungen treffen und die nächsten Schritte planen ist nicht einfach. Relevante Daten können einem die Entscheidung erleichtern. Doch wie fängt man mit datengetriebenen oder daten-unterstützenden Entscheidungen eigentlich an? Woher weiß man, ob man die richtigen Daten hat? Was wird zur entsprechenden Aufbereitung benötigt und wie kann ich die Daten entsprechend visualisieren?In dieser Episode geben wir einen Einblick in das Feld der datengetriebenen Entscheidungen. Wir beantworten, warum Tortendiagramme blödsinn sind, wie die Architektur aussehen kann, ob das Bauchgefühl überhaupt noch relevant ist und warum man nicht mehr sein eigenes JavaScript Frontend mehr bauen muss.Bonus: Was warmes Bier mit Husten zu tun hat und wie das Oktoberfest unsere Podcast-Statistiken beeinflusst.Feedback (gerne auch als Voice Message)Email: stehtisch@engineeringkiosk.devTwitter: https://twitter.com/EngKioskWhatsApp +49 15678 136776Gerne behandeln wir auch euer Audio Feedback in einer der nächsten Episoden, einfach Audiodatei per Email oder WhatsApp Voice Message an +49 15678 136776LinksWartungsfenster Podcast mit "Make or Buy": https://wartungsfenster.podigee.io/20-make-or-buyEngineering Kiosk #43 Cloud vs. On-Premise: Die Entscheidungshilfe: https://engineeringkiosk.dev/podcast/episode/43-cloud-vs-on-premise-die-entscheidungshilfe/Engineering Kiosk #12 Make oder Buy: https://engineeringkiosk.dev/podcast/episode/12-make-oder-buy/ClickHouse Datenbank: https://clickhouse.com/Google BigQuery: https://cloud.google.com/bigquery?hl=deQlikView: https://www.qlik.com/de-deTableau: https://www.tableau.com/de-dePowerBI: https://powerbi.microsoft.com/de-de/Amazon QuickSight: https://aws.amazon.com/de/quicksight/GCP Looker Studio: https://cloud.google.com/looker-studioMetabase: https://github.com/metabase/metabaseApache Superset: https://github.com/apache/supersetRedash: https://github.com/getredash/redashGrafana: https://github.com/grafana/grafanaOpen Podcast: https://openpodcast.dev/Engineering Kiosk Podcasts zum Thema Datenbanken: https://engineeringkiosk.dev/tag/datenbanken/@EngKiosk Tweet mit Metabase Statistiken: https://twitter.com/EngKiosk/status/1590373145793396736Sprungmarken(00:00:00) Intro(00:01:00) Cloud vs. On-Premise im Wartungsfenster Podcast(00:04:32) Das heutige Thema: Datengetriebene und Daten unterstützende Entscheidungen(00:05:16) Was verstehen wir unter datengetriebenen Entscheidungen?(00:08:18) Gefälschte Statistiken und die richtige Daten-Visualisierung(00:10:25) Wer hat Zugang zu den Daten und wie sieht die Daten-Transparenz aus?(00:14:05) Muss jeder Mitarbeiter SQL-Abfragen erstellen können?(00:15:55) Die Architektur für datengetriebene Entscheidungen(00:18:53) Pre-Processing, OLAP, OLTP und Datenbank-Normalformen(00:21:46) Was ist Clickhouse und welche Tools gibt es auf dem Markt?(00:22:59) Sind Tortendiagramme Blödsinn?(00:23:46) Die Visualisierung: Wie finde ich heraus, welche Fragen wir eigentlich aus den Daten beantwortet haben wollen?(00:25:53) Wie verwende ich Datenvisualisierung, ohne ein eigenes Team?(00:28:30) Schnelle Dashboards und Performance von Queries(00:29:28) Was ist bei Datenbanken in Bezug auf Analytics optimiert?(00:31:03) Muss man noch sein eigenes Dashboard-Frontend mit JavaScript bauen?(00:36:21) Welche Tipps würdest du Neulingen zur Dashboards-Erstellungen geben?(00:39:17) Ist das Bauchgefühl noch relevant?(00:41:30) Ab wann sind Daten aussagekräftig (statistisch signifikant)?(00:45:51) Welche Firmen sind Vorreiter im Bereich datengetriebene Entscheidungen?(00:47:29) Kann man zu datengetrieben sein?(00:48:21) Woher weiß ich, auf welche Daten ich gucke?(00:50:10) Outro: Podcast-StatistikenHostsWolfgang Gassler (https://twitter.com/schafele)Andy Grunwald (https://twitter.com/andygrunwald)Feedback (gerne auch als Voice Message)Email: stehtisch@engineeringkiosk.devTwitter: https://twitter.com/EngKioskWhatsApp +49 15678 136776

No episódio de hoje estamos com Andre Pretto, profissional com uma bagagem de 15 anos em Engenharia de Dados, trabalhando ativamente no mercado europeu.Suas stacks têm foco em soluções open source, improvement cloud no ks8 e streaming de dados.Veremos que o Click House é um banco de dados colunar de código aberto para processamento analítico online, usado em cenários que necessitam de análise de dados em grande velocidade.Por exemplo, a telemetria de IOT, análise de métrica, entre outros.Fique com a gente até o final, no nosso Engenharia de Dados Cast! Luan Moreno = https://www.linkedin.com/in/luanmoreno/

  I had a chance to sneak up on Vahe Torossian, a Microsoft Corporate Vice President and the man in charge of Sales for Microsoft Business Applications. While Vahe has been with Microsoft for 30 years, many of you may not know him, so I wanted to fix that. Vahe is no ordinary Seller; he's the “Top” guy who sets the sales strategy and motions for the entire global team. Vahe is also the guy who runs the really big enterprise customer meetings, and he's super-friendly, as you would expect for the Chief Rainmaker. We covered a lot of ground in this one, so enjoy! Transcript Below: Vahe: Hey, Vahe Torossian speaking. Steve: Vahe, Steve Mordue, how are you? Vahe: Hey Steve. In fairness let's say Charles mentioned that somehow you were going to call me. I didn't know when, but it's great to talk to you. Steve: After I interviewed him, I asked him who would be a good person to talk to? And he dropped your name. So it doesn't surprise me that he gave you a little heads up. Have you got a few minutes to chat? Vahe: Yeah, of course. Thanks Steve. Steve: Oh, perfect, perfect. So before we get into it, maybe we can tell the listeners a little bit about what your role is. I know you've been at Microsoft forever, I think like 30 years or something like that, and you've held a lot of different positions. But now you're in the business application space and that's been fairly recent. So there's probably a lot of folks that might not be familiar with you, who should be. Vahe: Oh yeah, thanks Steve. You're right. I've been celebrating my 30 years anniversary at Microsoft in April in 2022. I actually took the helm of the Biz Apps sales organization globally in late 2020. So basically I took my one way ticket to Redmond in December 2020. And the plane was almost empty, it was during the pandemic. And it was kind of a strange feeling for someone who has been traveling so much in the past. And of course, let's say I came with the lens of the business application, of course. Having led let's say Western Europe in my past role, having all the businesses of Microsoft. And I think Western Europe was quite successful on Biz Apps, our trajectory growth. And I guess that was also in fact the good match to some degree to try to take it at the global level. Steve: So is it a little easier to think about a smaller segment of the product mix, now really being able to focus like a business application? So I think before you were looking over all sorts of different things, weren't you? Vahe: Yeah, actually it's a great question. Because I think it's very different way of looking at the business. When you are, let's say almost you are the CEO of Microsoft in the countries that you are, let's say leading. You have all the levers to engage customers, partners, government, in different circumstances. And you try to leverage as much as you can the portfolio that you have to maximize the value. In the context of let's say the business application. I think it was, the interesting bet to some degree Steve, was to say, Hey, this has been a portfolio at Microsoft, whether you call it Dynamics 365 or Dynamics only as a brand in the past. And if you go back 20 years, let's say almost, with the Navision and Axapta, and Solomon Software and Great Plains. All these stories, all these product came together. And 20 years later, I think it has been part of a portfolio somewhere. Vahe: And you had almost what I will call the strong, let's say, portfolio of Microsoft, the platform, the modern workplace and environment. And I felt the work that James Phillips in the past, and with Alyssa, and Charles, and Amy here now on the marketing side. Have been a strong inflection point to bring together both the technology in the cloud environment. But at the same time, a market environment that requires very different, let's say tools to make the most of this transformation. And I felt that there's one piece at Microsoft that requires a huge catalyst leveraging the innovation. But responding as much as we can to what the customer need or even don't know yet what they need. And I think that's what I think to me was almost a bet. It's almost like all of a sudden you move to the little dog, if I may say. But with a huge potential of transforming something with great asset for Microsoft, and the customers and partners. Steve: Well I have to say, having been involved with Microsoft for a while, we have a phrase over here called redheaded stepchild, which is kind of what Dynamics was for many, many years. It was off campus, it was just this thing out there and under Satya, when Satya came in, he's the first one that I think came into the position that recognized this should be another leg on the stool, not some remote thing out there. And I think that's made a huge. Difference because I was involved in the years before Satya with business applications and they were not just something over here on the back shelf, and now they're right front and center. I think that between Dynamics and what's happened with the power platform, cloud in general. Microsoft's ability to get into and help customers is massively different than it used to be. And in your role now, you're dealing with a lot different type of customer. You're talking about Office 365 or Azure, you're dealing with IT. And now you're mostly dealing with business users. It's a completely different audience you're having to work with today, isn't it? Vahe: Absolutely. I think also you're right since Satya took the helm of the company, to some degree you of course we have seen how we tackle the cloud computing hyper-scale environment. But at the same time, in fact what happened with the Covid in the last two years, have seen an acceleration of what we call in the past, the productivity tools to become more and more collaboration environment. And from almost an application or a set of application, it became more and more a platform on its own. And so it's almost like when you think about where we are today and we were talking about the Covid, I don't think the Covid is yet over fully everywhere. But now everybody's talking about recession, right? And there's no one headline that you look, you say, oh my goodness, what's going to happen? Which just means in terms of planning for 22, 23. Vahe: So I think the assets that is now quite unique to some degree, or differentiated as you said, between the Dynamics 365 platform components and the Power Platform, it's almost bringing together. But I think, I don't remember Steve, in a few years back, I think Satya was talking about the mobility of the experience. And that was more from a device perspective initially. But actually what you see now is that with Teams as a platform, the system of productivity almost connect with the system of record more and more. And it's re-transforming the way you are thinking. It's almost like, you think about, you don't have to go to a CRM environment or ERP environment to get access to the data. It's almost like wherever you work, if you use an Excel or if you use Teams or whatever, you get access naturally, almost intuitively to your data set. And the data set are that's almost fulfilled naturally. And so we have no additional task. Vahe: And so I think that's the transformation world in which we are. Which connects cheaper well. We almost do more with less, right? And that's going to be almost the conversation we're going to have in the coming month. And it started already with many customers and partners. How we can optimize the assets that they have, how they can let's say increase the deep provisioning of some assets that they have. They are paying too much to concentrate a bit more, to get more agility. And I think this is where also, from a partner perspective, Steve, I see a lot of potential. You are referring to Power Platform, it's fascinating to see what it was in the very beginning, this notion of citizens developer, what does it mean? Vahe: People didn't know exactly what it is, we're quite afraid to touch it. But now when you see the shortage of developers in the market in general. And how you can make the most of some absolutely topnotch people who are not developer, touching the last mile execution challenges. Have been facing crazy environment and situation that they say, I can't believe how my IT guide doesn't solve these things. I've been telling them the customer pain point for so many years. And now with some, let's say [inaudible 00:08:45] place, let's say available for them, along with some let's say technical assets, you can really make the magic in the very, very, very time. Steve: Charles came up with a term on the fly, ambient CRM. Kind of where we're heading here when you talk about things like Viva Sales and some of these pieces that are really wiring all these components together. Covid was a terrible thing, but it certainly was a perfect storm for pushing the technology forward into a place that it's been fighting to get to, it's really been fighting to get to that point. And Teams was a great product. But certainly Covid created the perfect environment where Teams made insane sense for companies that were maybe just thinking about it or dabbling with it, and suddenly they're all diving into it. And you guys of course poured the investment on top of that. And I think that the silver lining of Covid, for technology, is how far it really allowed it to advance in that period of time. Maybe we just need a pandemic every five years to push a technology forward. I don't know. Vahe: No, but I have to say that even in my previous role when I was running Western Europe. Even the most skeptical people in regard to the cloud or the transition to a cloud environment. Having the one that rushed in the first, almost to a cloud environment, once the pandemic has been a bit of a real situation to face, and to drive the economy or the public services let's say on. So I think you're right, so you don't want to wish for another pandemic or whatever, but it has been absolutely a forcing function in many domains. And that's true. Steve: I think the challenge we have is particularly in the business application space. You guys have launched so many things in such a short period of time. And as you mentioned before, Power Apps, people picking it with a stick, they don't even know what it is. And there's also this first mover fear, I think. Microsoft has been, in my mind, kind of famous for coming to the game late and then just taking over the game. We were very late to the cloud, but once we got there we just took over the cloud, and it seems to be a pattern. But when you look back at the early days of cloud before you guys stepped into it, it was wild west. And all sorts of challenges with cloud. And I think that that gave a lot of people fear about, I remember I moved into cloud early and we got destroyed. Steve: And so I think there's a lot of folks out there, just from a technology standpoint, that have gotten their hands burnt by moving too quickly. And we're at that point with the platform and dynamics, where these are not new anymore. Relatively in history, they're new. But they're not new products and they're not built by some garage shop somewhere with a couple of developers. This is what 15,000 people building this stuff back there. This is professionally built, well built stuff, that is ready for prime time. So the first movers have already come through and they all survived. So I really feel like we're at that point where it should just take off now, it should just absolutely take off. And I'm sure you guys are seeing this. Vahe: Yeah. And Steve, I think one thing also is that you're right, there's a usual thing about let's say the first mover advantage. At the same time from a customer perspective, you don't want to be the Guinea pig, right? On any situation, especially from the technology standpoint. I think that increasingly what I see in the conversation is that there's almost now, because of the quality of the native integration of the several different applications. Whether you are in the customer experience environment, on the service side, on the supply chain, on the finance or the local no code or app. All these components are absolutely connected to each other. And basically whether you have Teams as a platform in your company, or Azure in environment, all these component are connected very, very easily to each other. Vahe: And so I would say that the beauty of it now is that you have all almost the notion of marginal cost. If you really want to leverage many of the assets that we can bring, and you don't have to take all of them at once, of course it has to be matching what you need now. But the right is that, let's say there's an almost fully integrated benefit all the connectors with the rest of the world outside of Microsoft environment, which is a great value for the partners, ISV and [inaudible 00:13:58], and at the same time to the customers. Who think now, hey I should do more with less. How should I think about my investments for the next, let's say five years? Most of the customers now are really thinking about the longer term relationship. And defining what's the value SLA almost that you're expecting both from the partner of the vendor and the vendor itself. Vahe: And so it's almost like, you remember when we transition from a world of build revenue and licensing, to now more consumption and usage. It's almost the user and consumption discussion is a forcing function about the customer success, how we align on the same definition of the customer success. And what's the time to value that you committed? What are the key milestones, in full transparency, that you need to bring in? And I think that's where we are now. And because Microsoft, I think overall as a company, have been increasing tremendously the level of trust. From the security standpoint, the compliance components, and so on, and the scalability. Vahe: I think that's the great leverage for us now in terms of the conversation and making sure that the customers are getting the value that we have been selling to them. How we show how much skin in the game we have to make them successful. And then it's a flying wheel. It's almost like the innovation will help you to bring new things, respond, anticipate, take the feedback of the customer to the engineering, develop new stuff quickly to the market. So I think it's what we are heading to now, Steve. And I think from a partner perspective you might even see and feel it, right, more and more. Steve: Oh yeah, I mean I think the sales motion has changed completely. Only a few years ago we go into a customer and try and convince them to replace Salesforce with Dynamics. And they'd say no, and we were done. We'd say okay, well we'll come back in a couple years and ask again. We had nothing else to sell them. And now today, I mean if they have Salesforce, fine that's great, keep Salesforce, let's add some things around it. Salesforce will work with Viva Sales, Salesforce will work with Power Platform. Steve: There's so many doors now, I think, for a seller to be able to get into a customer and solve problems for that customer without having to do the one big yank and replace. Which is very difficult to do, it's difficult to do on opposite as well. I mean once a customer gets a big solution like Salesforce or Dynamics 365 installed, those are very difficult to uproot, it takes a very long time. And you guys have created now, this product mix, where we don't have to uproot something to sell that customer and to get engaged with that customer. We can go all over that business without having to uproot something. And I think that's huge. Vahe: I agree Steve. And I think that it's almost this notion of rip and replace type of strategy, right? In some cases it works because this is what the customer wants. They are fed up about let's say competitive environment that didn't deliver on the expectation. And we should be ready to cope with that and respond, and we have a lot of this. But at the same time as you said, what we call the strategy of having a hub and spoke, let's say, almost environment, gives us for every line of business. That we decided as a company to go and have a significant acceleration of growth and market share, is very much to give that option to say, Hey, you know what, Mr. Customer, Mrs. Customer, you decide to be on that type of environment, who we are to ask you to change? Vahe: If you are happy that's fine. But what we can bring you is almost to enhance what you have with some component that absolutely will be transparently integrated to what you're using. And it's a great circuit, an additional circuit for the partner, it's a great value for the customer. We don't feel harassed to change something because we know the cost of transitioning from one to another one. And then it's up to us to demonstrate the value we can bring and eventually we can take from there to the next level in the future. Steve: It's got to put some pressure on the competitors also. I if think of, I might just use Salesforce because they've always been the big competitor. I'm sure that they were confident sitting there at their large customer when all we had was trying to replace their instance that was going to be difficult to do and then we'd go away and they didn't have to worry about us. Now we're coming in and we're circling around, and we're solving problems in this department, and we're building apps in this department, and we're literally bolting into Salesforce. And one potential outcome is that the customer decides over time that wow, all of this Microsoft stuff that we've brought in works really, really well. Steve: That's gotta put some pressure on the incumbent big application in there that hey, you're surrounded by a bunch of stuff the customer is very happy with, you better make sure they're happy with your stuff and they don't reach that point. Cause like you say, oftentimes when you see those rip and replace, it's because the product, or the company, or something hasn't met the expectation. And to be fair, that could actually happen with any of us, right? It has a lot to do with implementation, design, how thing was put together. Less to do with the application itself, that could happen to any vendor. But certainly raises the bar to some of these competitors when they're surrounded by well performing Microsoft products that are satisfying customers. Would you think? Vahe: Yes. Absolutely. And that's why there's a continuity between what we sell, how we sell, to who we sell, and how we drive the implementation. It's an ongoing wheel that is a very different mindset that we all learn in the transition to the cloud, let's say, environment. But absolutely. I think it's a good forcing function to raise the bar to some degree, raise the bar for the benefit of the customer. You mentioned the competitiveness of what this type of hub and spoke strategy can create. You're right. But in the end, the biggest, let's say winner, will be the customer, right? Which I think is always and should always be the north star for us and our partners. Vahe: And I would say the relevance of the innovation should be in fact the pressure that we put to each other to make sure that say we listen carefully to what the customer is facing as a challenge, but potentially to translate their current challenge into the future challenge, to push them also to think differently. Because I think the notion of rip and replace [inaudible 00:21:06] One of the thing was, I don't know if you remember that the initial issue and worry was that people were saying Oh, we are moving to the cloud, therefore we are transforming. Well it was not that tried and true. People were just keeping the same processes in the cloud and the one that they had on premise. Which was not benefiting at all of the scalability and the agility of the cloud environment. Yeah, you remember that right? Yeah. Steve: They just changed the way they were paying for it. Vahe: Absolutely. Absolutely. So I think that's what we have seen on this application modernization, on some of the enterprise wide innovation also opportunities that we had discussed, is how much you can really say, in this new world of competitiveness, of un-expected challenges. How you can, let's say, keep your applications fitting always in fact proactively the challenges that you're going to have too. As opposed to keep going with a quite heavy code to maintain, with people who leave that cost you a fortune to maintain. So I think this agility that the power apps, [inaudible 00:22:22] to made, have been bringing I think is the reason why we have seen this huge acceleration of growth, which is today is six, seven times faster than the market growth of local no code. Vahe: So I think it's a great, let's say indication, of what people start to realize. And I think in the conversation that you had with Charles when he was referring to, hey some of the AI capability have been slower to be picked up by the vast majority of customers. And it's true because there's a level of, let's say, can I trust this thing? Am I going to lose completely ground and control of what I'm doing? All these natural thing. I think as we bring more and more, let's say tools, are manageable. The Power Platform environment, or let's say the device sales capability on top of the teams or Salesforce environment. That people will start to test this. Vahe: And I think we're going to be more and more advocate about Hey, what are the benefits of the organization that are using this technology and how we can trust them lean forward. And I think Charles was referring to our digital sellers. Their daily life is very much, let's say, using all these AI lead capabilities in terms of reporting, in terms of let's say incident management, in terms of even coaching for themselves to do a better call next time, is just fascinating to see. Maybe we should even do a kind of, let's say talk on this, once we have a bit more, let's say after the GA, maybe a few months after, we should have, let's say what the key learnings and [inaudible 00:24:00] from a customer standpoint. Steve: Yeah, it always makes a customer confident when they know that the vendor is using the product that they're trying to sell them. It's interesting, everything moving to a subscription has changed kind of the mindset, not just of you guys obviously, where there's no big sale. There's a sale of a big subscription, the revenue of which will come over a long period of time. But the customer has this option every month to say, you know what, I'm not happy, you're not solving my problem. In the old days they were kind of stuck, they bought all this stuff and they had to make it work. Now they don't have to make it work, we have to make it work, we have to keep them happy enough. Steve: We recently launched a professional services on a subscription, which is an interesting model, that I lay awake at night thinking about that same thing. That before a customer would pay you a bunch of money to a bunch of stuff and now they're paying you a little bit of money every month for as long as you keep them happy. And this bar of, I mean we've always wanted to keep customers happy. But it's never had the impact or importance that it does when you're on a subscription with that customer who can just any time say, “I'm not happy, goodbye.” It raises the bar I think for you guys to have to continuously innovate, what do you done for me lately? You got to continuously innovate and bring new things. And you've got more motivation probably than the company's ever had in history because of the subscription model. Do you feel that internally? Vahe: Yes, yes. As I said, it has been a great enabler to raise the bar. And it's almost like you know can have a beautiful slide deck and saying the right things, but the execution doesn't match what you are saying somehow, that you don't walk the talk. I think you could have been in that situation in a kind of on-premise environment. I think the cloud has been a forcing function to say, hey you know what, you can claim you are customer success, or you are customer first, or you are customer obsessed. But the reality is that if you don't deliver the service properly, if you are not as responsive timely, if you're not proactive, customer will say enough is enough, I can stop my subscription. Steve: I have options. Vahe: I have options. So I think it's a good hygiene, how it makes you having an embracing habits, that I would say are the natural thing when you engage with customer. But I think it's almost, let's say, for the one who might have forgotten that basics, it has been a great, let's say, opportunity to bring back the roots of what is it to satisfy a customer, right? And I think that's what the cloud licensing model helped put together. And I think there are still always room for improvement. Vahe: And similarly I would say, what you have seen on the collaborative applications, what we have seen on the low-code, no-code, you are going to see it now, also I would say on the supply chain environment, which is shipper, shipper at stress because of what we have seen on the Covid, but also in fact on the geopolitical aspect and some of the recession discussion. And also, on the overall, what I would say the contact center in our environment at large. How this world is going to change is going to be led a lot by the capability that technology can bring, and the ability to listen carefully to the strategies and the challenges of the corporation that are involved in. So it's quite exciting actually. Steve: I don't get involved a lot with the call center operations. But I picture the old call center is this massive building full of cubicles and people with headphones. And I picture that now that most of those people are probably working remote. A call center now could operate at my desk, just about, and have thousands of people all working from their home. So, that whole industry feels like it's changed significantly. And yes, I'm sure they're starving for the technology that fits the model that they're being pushed to adopt. Vahe: Yeah, yeah absolutely. I mean it's interesting, if you summarize some of the business challenges or the things that are coming from multiple conversation. We had the nuanced [inaudible 00:29:04] a few months back. And so it's almost the first fiscal year where we're going to be able to strategize, operate together as one organization. And it's great because somehow you take their own experience in terms of conversational AI and what they have been leading in for many years. And at the same time you hear both, let's say, the customer feedback when it comes to, as you said, the traditional contact center or call center evolution. How to translate this into a modern service experience, right? Vahe: And how AI can contribute to that on the seamless integrated way. How to think about customer retention in this world where people are a bit more struggling with their bottom line. How to protect the customer privacy as well. Because you talk about voice capability and recording, but how you cope with the privacy and the security during this service journey. So all these are absolutely great opportunities for us to combine what we're hearing, the technology and the acquisition that we did a few months back, to put that into a great component. And I would say the data analytics that the power Platform Power BI gives us on the back end, is going to be a great platform for us again to differentiate from the rest of the world. Steve: Well and it'll also help kind offset the fact that these people are all remote now, right? They used all be sitting in this big room. And people were standing up there looking over a rail at them making sure they were doing what they were doing and available. And you can't lose any of the customer service quality just because you've moved everybody out of the building and nobody can physically see them anymore. AI is the only way to plug that hole really of being able to know what's going on in this organization with all those people remote. In your day-to-day activities, I'm assuming that since you're head of sales that you get engaged with all of the big opportunities that come to Microsoft. And you're in there leading the charge to get them to make a decision for the services. What are the areas that you're seeing among those larger customers that they're really excited about? Is it the low-code stuff, is that very exciting to them? Or are they still wrapping their arms around that? Vahe: No, no. I would say that the notion of, let's say, application modernization, which doesn't mean I do the same thing I was doing before in the cloud. Really thinking about, what do I want to fix? And how much I can include some perspective about what could happen in some, let's say options or scenario? That capability that Power Apps has been giving them. And now we see that the corporations who are the most successful are the one who are almost creating a center of excellence within their own organization, that let's say help the IT to monitor someone, in fact the usage rate. But also to amplify the user experience and to spread it across the organization. And the ability to almost measure the positive impact. Vahe: The second thing I've seen is on the low-code, no-code, is the time to value. It's almost like you can almost now, and when I say “we,” it's almost we with the partners. We can almost say for this type of let's say expectation, or application, or challenge, it will take three month to be ready, not three years, two years. Or we have a heavy development environment. And so this center of excellence, let's say mindset or framework, is a very powerful one. Because it helps to almost create a concentration of hey, what are the most critical things to fix and how long it's going to take? Vahe: And people are almost, let's say very impressed, about how quickly you can have great quality because you bring both the expertise of, as I said, almost the person who is facing the challenge every single day. Being non-technical guy, we have in fact the support of IT. And I think that's the business decision makers along with the IT. I think to me, that's why we have been on this six, seven times faster than the market rate. We have huge ambition there. And be aware that we have also 20 million of users of Power Apps today that came from the city campaigns. So people are actively using it, not yet paying it. So that means that it's great, it's the future almost by, for us to go after. Because people are starting to use in fact at least the basic functions to get adjusted customers to and so on. Vahe: The second thing I would say is that people have realized how easy it is, and recognizing that Teams became a platform close to 300 million users. It started at 25 or 30 million almost pre-pandemic. And so that became, almost as you said, you are at home, or you are wherever you are and that's the interaction that you have with your customers, partners, ecosystem and employees. And so now it's a marginal component to say hey, can I have one tab that is going to do that type of task? My forecasting, my thing. So this is again the connection between what you use every single day at scale, and the marginal cost of bringing a component of Dynamics 365, a component of the application that you create quickly for Power Apps or Power Automate from the process, implementation, and automation. So I think that's what I see the two biggest part of the customer reaction, and I would say feedback for us. And encouragement to be fair, to keep going in that direction. Steve: We've got lots of examples that you guys have got out on the case studies of large companies that have really got in head first. And just thousands of apps in the organization solving thousands of problems. And just excellent, I mean you just have to almost grin when you look and hear about these things. But for every one of those there's still a bunch of them out there where, I don't know, IT maybe is still an obstacle. I mean IT has been, it's interesting because IT's been a friend of Microsoft for a long time because a lot of the products that they have engaged with were Microsoft products, servers, et cetera. They've had to make this transition to cloud, which was scary for them. But they ultimately did it for the most part, not all of them, did it. And now here comes low-code, no-code that's got to scare the bejesus out of a lot of IT folks. And how are you at that company size? Because frankly, we struggle with the same thing in the mid-market. How, at that big company size, do you deal with that occasional obstinance from it? Vahe: Yeah, it's a great point. You're right. I think Microsoft in general, I don't want to generalize, but in general have been for the last four years, very, very close to the IT decision makers. And rightfully so, because there were so many and still so many things to achieve in partnership with the IT and CIO environment. At the same time, when it comes to business applications or business process, I would say that you need to find the balance between the business decision makers, who are the ultimate decision makers when it comes to what is going to affect their business, or the way they work from a Salesforce perspective, or the way the marketing leaders wants to automate some of the processes that they believe is important. Vahe: And so that we probably are in a unique business case at Microsoft, where you have to talk to both. And the learning is that in the very beginning where you were only talking to IT, for example in the low-code, no-code, you could have signed a deal with IT, but then you know almost had to start to sell it again internally. Because you had to knock to all the doors of the business decision makers to say, Hey, do you know that you have this thing in your corporation, and anyway this is the thing that you can do, do you mind starting over there? Vahe: And so that was basically almost a waste of cycle. And so we said we have to do these two things together. We need to be able to articulate what is the value of low-code, no-code, maybe in FSI, financial service, or manufacturing, or in retail. And of course there's a strong common denominator. But there are some specifics that may resonate more for some industries more than others, and therefore the decision makers. And we have seen that when we do these things well together in parallel, when you sign the contract, or the deal, or the agreement, the time to move to usage or the business case implementation is much faster. Basically you bring more value both to IT and the business, and for Microsoft. And so I think that's the piece where I think it evolved on low-code, no-code, from being afraid in the beginning or skeptical, to a place where they are increasingly embracing this center of excellence environment. Where they own it as [inaudible 00:38:55]. It is connected to the business decision makers, therefore it brings value. Vahe: And so IT brings value to the business decisions or the business unit and the line of business. And then what was missing so far was, how can we give them the monitoring environment, almost the control board to manage the budget, to manage let's say, or having warning to say, hey, business A, you know are over consuming. Should we lower the investment or should we accelerate because of what you are doing? So I think that the kind of tools that we are bringing now to the IT, so that they are absolutely part of the success of the company and they are connected to the business decision makers. I think that's the best way for us to demonstrate value and keep it completely aligned with the business directions. Steve: And the opposite would be true also if you're going in trying to sell the line of business owner without talking to IT. And you convince the, now you got to go sell IT. So it's two cycles. Vahe: Absolutely. Steve: You have to somehow get them both in the same room and do it at once. So we've got so many products coming, we've got so many products here. And if you imagine a generic customer of a large size that you're going to be going to talk to next week about all the Microsoft has to offer. What are a couple of the key products that you're going to want to make sure you land in their head, that you feel across all companies are extremely high value or differentiators? The thing you don't want to walk out of that room without mentioning? Vahe: Yeah, I would say, and somehow you touch on it Steve, earlier on. As part of the transition that we are driving, one of the thing is also to simplify. To simplify the portfolio, to simplify the go-to market, to simplify the strategy. We discussed the hub and spoke, let's say strategy. And so I would say at the very beginning, what we said is that instead of saying, hey, there's a proliferation of products. And every year we add more and more and more. And at some point you confuse your own sellers, you confuse the customer, you confuse the product, it's super tough to digest everything, and even understanding what's the hierarchy across all these things? Steve: For licensing Vahe: And licensing on top all this complexity, right? I mean we have gone through it, and it's still not perfect. But at the same time I think what we said is that there are the categories, or the line of business, that we want to go in. We want to have a fair shot to take a leadership position in the next let's say years. And what it takes to get to that point, from an innovation perspective, from a go-to market perspective, from a part program perspective, from a sales and seller investment capacity perspective. And so on. And so I would say that's more the starting point Steve, where we say we define five categories, a fine line of business, where we believe we have a shot to become a leader. And these categories we need to be able to be clear on where the value that we bring. Vahe: For example, if you take the customer experience, let's say OLAP, which is more the connected sales and marketing, if I may summarize at the high level. It's going to be all the conversation about the collaborative apps, the customer experience transformation. You have already Teams for the vast batch of you, hey that's what you want to achieve. The Dynamic 65 sales is going to give you that capability, or the LinkedIn Sales Navigator on top of it is going to give you that type of insight. You know are not touching about AI, you think about almost sales automation, Salesforce automation. Let's show you how the AI infused capability within Dynamics 365 sales and marketing, give you that asset absolutely naturally integrated on your team's environment. Vahe: And same thing on Viva Sales, the sales productivity, we can measure it the way you want, and you're on control of that. And by the way, if it works on the environment that you are working, could be Microsoft, could not be as we discussed, that's more the conversation that we want to have. And of course on the back end you are going to have Dynamics 365 sales, and marketing, and Viva sales, most of the time for that line of business. If you think about let's say low-code no-code, I would say you will have probably three type of conversations. You know will have a conversation about hey, you're a large enterprise, multi-deals coverage. And basically the benefit of having an enterprise wide, let's say engagement, what does it mean? What's the framework for you to make the most of it? And how we commit with our partners to deliver you the value. Vahe: And so you can commit on five years maybe with Microsoft and how much value we can bring already to you. Or it's purely an application modernization. You move to a hyper-scale environment, but you have all these old fashioned applications. So basically, you are a platform that is modern but all your application are still old fashioned. How low-code, no-code is going to help you to accelerate that transition. And let's start with one company, one app. Pick one and let's do it right, and then replicate from there. And then potentially, in fact, the last one which I think is going to be the biggest one potentially, is the business process automation. Think about the forecasting process. I have to say that when I was running my business in Western Europe, we have been doing this traditional forecasting process, which in every company when we talk with business leaders or CFOs, that's the same thing. You ask the forecast at the lowest level of the organization, then the manager of that organization, do a judgment. That judgment moves to the next level of management. The management do another judgment. Vahe: So all the way up to the top level, who does a judgment anyway on top of it. Or they find, depending on who is doing the forecast, almost let's say a coefficient of let's say correction based on who is doing the forecast. When you start to do that thing into AI and you say what, we know the behavior of people [inaudible 00:45:26] potentially, you come after 18 months or one year to a trend of forecast that is so close to in fact what you were getting before. That you say how many hours, thousands and thousands of hours of productivity saving I'm going to have just because of this AI forecasting capability? That's the kind of example of it, for say an application for low-code, no-code, that is just checking in fact the behavior or the intelligence so far to help you to drive your business. Vahe: And so we have been running that internally as well and it's quite impressive. And so that's the kind of conversation that you want to have both with the IT, but you see this perfect example of hey, having that conversation with the CFO, or the sales leader, is a great one. Because it's a marginal cost again, to what you are using already. And the same thing happened on finance, and supply chain, and service when it comes to, all right so where you, what are you using? Are you still on-prem? The vast majority of ERP, the vast majority of contact center and call center are still on-prem. So you can think about hey, what does it take to move to a cloud and more agile environment? What are the best that you want to do? Which is the strategic partner or vendor, who are going to take this? Because you're not going to change this environment every two years. It's a 5 year, 10 year bets, right? Steve: The marriage. Vahe: It's a marriage. Yeah, absolutely. So I mean does it help Steve? Steve: Yeah. And I think interesting, one of the things I think about AI in forecasting, is it doesn't have any personal bias. And obviously in larger companies I'm sure there's a lot of checking and cross checking. In the middle market it's a bunch of optimistic sales people coming up with optimistic projections that have no basis in history or anything else that's going on, of what's going on. And I've been in meetings where we've been displaying some AI facts, or figures, or forecasts, or projections. And listen to senior people just adamantly disagree. That number is absolutely not correct. And I've had them tell me I've been doing this for 30 years, I know, I know. And then here comes next month and guess what was right? The AI model was right and the guy who's been doing it for 30 years is making up some excuses. Steve: So I think that the world right now is fraught with bad projections on everything. Cost projections, sales projections, there's too much personal bias involved in the process of creating those things. And as leadership of a company, you're relying on these things. They're going to drive you right over a cliff potentially, if you're not careful, if you don't have good information, if you can't get the bias out of it. And I think that's one of the big things that AI brings that I've found resonates with leadership sometimes, is kind of remove all the bias. I mean it's just removing all the bias. You don't want to hear smoke, you know want to hear reality so you can act accordingly. You're surrounded by a bunch of people who want to make you feel good, but AI doesn't care how you feel. It's going to tell you the truth, doesn't care if you get mad. Vahe: Steve also, it's interesting because sometime, you point to this that sometime when you are too early on the innovation, some people might be again scared or skeptical as we said. But I remember we were looking at let's say some numbers when it comes to, are we operating consistency, for example, in the world? Or there are some that say practices that are bringing more growth or more relevant than other places. And so, one thing was interesting was in the services line of business or category, you think of case management. And it's one of the opportunities. And you might say well case management is not super innovative. Well, it's something that is quite well known. But case management was one of the fastest growth in majors. And that was because it was responding to the fact that vast majority of the case management processes are still on-prem today. Vahe: And the one we're moving to the cloud, especially in public sector, to make sure that the queuing system is working, you have a full up, let's say email to tell you and tracing where you are on the request that you put in place. All these things we believe is generic everywhere, but it's not, it's by far not. And across mid-market, and large corporation, and private sector, and public sector. So it's not always innovation that drives in fact the next generation of work. It's also in fact the basics that are not fulfilled today and that create a bad customer experience. And that's interesting, in a way, to keep very humble about let's say what we still have on our plate. Steve: I can remember not that long ago, when you talk about customer service, the goal of many companies was to provide as bad as service as possible so they didn't have to do it. I mean it was a cost center for them. They hadn't come to the realization yet, this is decades, but hadn't come to realization yet that customer service is what drives future revenue. They just looked at as a cost center and figured the worst it is, the less people will use it and it'll cost us less, so that mindset has changed. You talk about fears that people have of technology. And so a lot of this is people self preservation fears. They see something coming, we saw it even in the partner channel, uh-oh here comes low-code, no-code, customers are going to be doing all the work themselves, they're not going to need us partners anymore. And it's like this first reaction that people have about anything new, is how's that going to affect me? And generally they're going to assume negatively. Steve: Our business is busier than we've ever been as a result of low-code. So it's actually been the opposite. But partners, and just like people, you know need to be prepared to pivot into that wind. If you're just going to stand there with your arms crossed and not move, yeah low-code's going to hurt you. You know need to lean into that. And the same thing with individuals that are looking at new technology. It's coming and you can either stand there with your arms crossed and let it knock you down, which is a foregone conclusion. Or you can bend with it. And to be honest, the younger folks are more flexible than us older folks. So they're not having any trouble with this technology at all. We recently signed a new customer, it's all young people and man they just get it. I mean there's no explaining anything. They understand every single thing you're talking about, why and what. And I mean they're born with a cell phone in their hand. None of this is foreign, but we still got to get rid of all of us old guys. Vahe: I agree, I agree. And time flies. And it's almost like, often, let's say, you need read to embrace that. Always a zero regret strategy in this type of, let's say, evolving environment. Anything that you postpone, to some degree, is almost let say a loss. And that has been proven in the technology run. And when I look at, we always have to be humble. It's a highly competitive market, and people are smart, and that's great. Cause as we discussed, it's all good for the customer. But I think that when I look back to the commitment of the company, the investment that we put in place last year with the support of Satya, Amy Hood, [inaudible 00:53:27]. With more than 1000 sellers injected in the marketplace, we keep going on the investment on the local no-code, even more so to drive the acceleration of the growth in addition to the Dynamic 365. Vahe: When I look at every category that we are in now, and I think it's a good confidence level that we on the path here. That first of all, we are between two times and three times the growth of the market for each of these category, that's a good indication. And I think that also raise the confidence level of the product sellers at Microsoft. To bring these different components together and add more value to the customer. So look, it's a journey Steve, and it's quite exciting to be on this. And people like yourself because we have been there also for a long time, and you know what it takes to transition. And you never fail, you learn always. And everything that you learn and that works, it's almost to think how we can scale and bring that to the mass as quick as we can so that people can benefit from it. Steve: Well success breeds success. And you know guys have got it going right now. I've taken up enough of your time. Anything that you want to get out there that I didn't ask or we didn't talk about? Vahe: No, I think, Steve, you did a good overview of let's say where we are, how we think. Again, I think that the simplification, the portfolio, the much more focused approach, the category, and more consistent execution on the go-to market is really the next level for us. And the hub and spoke strategy across all these categories gives much more room to increase the business opportunity for us and the partners. Steve: Yep, I think so, I think so. All right, listen, it was great talking to you, I'm glad you made the time. And I definitely hope to able to talk to you again in the future, get something new to talk about. Any time you want to reach out, and jump on, and talk about some stuff, let me know. We're happy to get you on. Vahe: We are all, let's say reading all these, let's say headlines on the recession. In a few months from now, between now and then of calendar year, we're to see a bit more clarity on how the planning is happening for the mid-market, large corporation, how the public sector is evolving in this dimension. And also, we'll have a few, let's say product launched that we talked about, Viva Sales, any learning from that, let's say maybe the first two, three months, would be interesting to see how people react. And maybe that could be a great opportunity for us to chat. Also what's going on the [inaudible 00:56:17] Steve: Yeah, yeah. Vahe: Plenty of things to talk, I guess. Steve: Sounds good. All right, well hey, thanks again for your time. Vahe: Thank you. Take care Steve, have a great day.

With a background in finance and a history of great communication and a passion for problem-solving, Ed Hansberry, an Assistant Director with P3 Adaptive, embodies the spirit of P3 Adaptive. Ed was recently awarded his 13th Microsoft MVP Award so of course, we wanted to know more about his achievement, his passions, his adaptability, and most of all, his insights on change so we invited him to join us today for a chat. Early on, in the conversation, Rob and Ed delve into defining change and that led to a lively discussion on the process of change, the successful process of change, and the difference between them. Here's a hint, it's always the people! Knowing that people drive success, Ed extensively volunteers his time in the Microsoft Power BI User Community supporting users around the world with problems and questions around Power BI and, since he has been recognized as a Super User by Microsoft, we really can say that helping people is his superpower. He is leading change one question at a time. This episode isn't just about change, though, the evolution of technology and software is embedded throughout the conversation from cube functions to the hidden power of the innocuously named OLAP dropdown in Excel. And finally, we get some great insight on formerly cutting-edge technology that has since gone obsolete. We hear a firsthand account of the tragic end of the Microsoft phone.  You never know what you will learn when the conversation starts to flow. As always, be sure to leave a review on your favorite podcast platform and tell a friend about Raw Data by P3 Adaptive, where data meets the human element. Also on this episode: iPaQ N NTP Not Necessarily the News NNTNs: All about Sniglets That Tufte book . . . MDX in Excel Cube Functions in Excel Disconnected Slicers with DAX Variables & SELECTEDVALUES Field Parameters in Power Bi Skynet Yoda Chong and the Treehouse of Wonder, w/ Donald Farmer Tabluar Editor DAX Studio A Single Complete Leader, w/ P3 Pres & COO Kellan Danielson Who Moved My Cheese

LINBIT SDS is a software-defined storage, that perfectly fits with Red Hat's OpenShift. It provides persistent volumes, exposed through the CSI interface. You can use it to build out of internal storage devices the persistent volumes for your cloud-native container workload.While it was initially designed for the on-prem data center, it is also a fit for multi-cloud and edge deployments. It is a good fit for IO demanding workloads, like OLTP and OLAP databases, message queuing, and AI.

ClickHouse, the lightning-fast open source OLAP database, was initially released in 2016 as an open source project out of Yandex, the Russian search giant. In 2021, Aaron Katz helped form a group to spin it out of Yandex as an independent company, dedicated to the development + commercialization of the open source project. In this conversation with Tristan and Julia, Aaron gets into why he believes open source, independent software companies are the future. And of course, this conversation wouldn't be complete without a riff on the classic "one database to rule all workloads" thread. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

This week, we're busting myths around Cloud Spanner with our guests Pritam Shah and Vaibhav Govil. Mark Mirchandani and Max Saltonstall host this episode and learn about the fantastic capabilities of Cloud Spanner. Our guests give us a quick run-down of Spanner database software and its fully-managed offerings. Spanner's unique take on the relational database has sparked some myths. We start by addressing cost and the idea that Spanner is expensive. With its high availability achieved through synchronously replicating data, failures are virtually a non-issue, making the cost well worth it. Our guests describe other features that add to the value of Spanner as well. Workloads of any size are a good fit for Spanner because of its scalability and pricing based on use. Despite rumors, Spanner is now very easy to start using. New additions like the PostgreSQL interface and ORM support have made the usability of Spanner much more familiar. Regional and multi-regional instances are supported, busting the myth that Spanner is only good for global workloads. Our guests offer examples of projects using local and global configurations with Spanner. In the database world, Vaibhav sees trends like the convergence of non-relational and relational databases as well as convergence in the OLTP and OLAP database semantics, and he tells us how Spanner is adapting and growing with these trends. Pritam points out that customers are paying more attention to total cost of ownership, the importance of scalable and reliable database solutions, and the peace of mind that comes with a managed database system. Spanner helps customers with these, freeing up business resources for other things. This year, Spanner has made many announcements about new capabilities coming soon, like PostgreSQL interface on spanner GA, Query Insights visualization tools, cross-regional backups GA, and more. We hear all about these awesome updates. Pritam Shah Pritam is the Director of Engineering for Cloud Spanner. He has been with Google for about four and a half years. Before Spanner, he was the Engineering Lead for observability libraries at Google. That included Distributed Tracing and Metrics at Google scale. His mission was to democratize the instrumentation libraries. That is when he launched Open Census and then took on Cloud Spanner. Vaibhav Govil Vaibhav is the Product lead for Spanner. He has been in this role for the past three years, and before this he was a Product Manager in Google Cloud Storage in Google. Overall, he has spent close to four years at Google, and it has been a great experience. Cool things of the week Our plans to invest $9.5 billion in the U.S. in 2022 blog A policy roadmap for 24⁄7 carbon-free energy blog SRE Prodcast site Meet the people of Google Cloud: Grace Mollison, solutions architect and professional problem solver blog GCP Podcast Episode 224: Solutions Engineering with Grace Mollison and Ann Wallace podcast Interview Spanner site Cloud Spanner myths busted blog PostgreSQL interface docs Cloud Spanner Ecosystem site Spanner: Google's Globally-Distributed Database white paper Spanner Docs docs Spanner Qwiklabs site Using the Cloud Spanner Emulator docs GCP Podcast Episode 62: Cloud Spanner with Deepti Srivastava podcast GCP Podcast Episode 248: Cloud Spanner Revisited with Dilraj Kaur and Christoph Bussler podcast Cloud Spanner federated queries docs What's something cool you're working on? Max is working on a new podcast platform and some spring break projects. Hosts Mark Mirchandani and Max Saltonstall

Frank McSherry, Chief Scientist at Materialize talks to Host Akshay Manchale about Materialize which is a SQL database that maintains incremental views over streaming data. Frank talks about how Materialize can complement analytical systems...

When you think about selecting a database engine for your project you typically consider options focused on serving multiple concurrent users. Sometimes what you really need is an embedded database that is blazing fast for single user workloads. DuckDB is an in-process database engine optimized for OLAP applications to speed up your analytical queries that meets you where you are, whether that's Python, R, Java, even the web. In this episode, Hannes Mühleisen, co-creator and CEO of DuckDB Labs, shares the motivations for creating the project, the myriad ways that it can be used to speed up your data projects, and the detailed engineering efforts that go into making it adaptable to any environment. This is a fascinating and humorous exploration of a truly useful piece of technology.

Alex Merced introduces the new DataNation podcast that will be available on all podcast providers the discusses what is OLTP and what is OLAP and why they matter. Join the DataNation community at https://www.DataNation.click Register for the Subsurface Conference at - https://www.dremio.com/subsurface/live/winter2022/?utm_medium=social&utm_source=dremio&utm_term=alexmercedsocial&utm_content=na&utm_campaign=event-subsurface-2022 Join the Subsurface Slack Community at - https://join.slack.com/t/subsurfaceworkspace/shared_invite/zt-ghkyk4ox-8FmydCM_6xGdx9Li0qo3Jg

Você gostaria de compreender a diferença entre Data Warehouse e Data Lakehouse e ir além para entender de fato o que acontece na realidade das empresas que adotam essas soluções? O Orlando Marley é um dos grandes especialistas nessa área e com ele, iremos dar dicas de como entender melhor esses dois paradigmas e como você pode unir essas duas soluções para entregar um Analytics marcante para sua empresa.Data Lakehouse é um novo conceito que vem ganhando tração rapidamente e para você poder se destacar como um engenheiro de dados se faz necessário aprender sobre. Luan Moreno = https://www.linkedin.com/in/luanmoreno/

Nesse episódio você irá descobrir um novo banco de dados que realiza transações distribuídas e que é totalmente pronto para o kubernetes.Iremos navegar nas diversas características e recursos do banco de dados open-source que foi inicialmente desenvolvido dentro do Facebook. O YugaByteDB oferece suporte as apis de Postgres e Cassandra ou seja, você pode de forma transparente mover seus workloads com mínimo esforço para dentro da plataforma.Transações [ACID] Distribuídas = http://bit.ly/3ui2flxRaft-Consensus = http://bit.ly/3uie7Eq Luan Moreno = https://www.linkedin.com/in/luanmoreno/