Podcasts about relational databases

On this episode of the Crazy Wisdom Podcast, host Stewart Alsop is joined by Yury Selivanov, the CEO and co-founder of EdgeDB, for a fascinating discussion about the reinvention of relational databases. Yury explains how EdgeDB addresses modern application development challenges by improving developer experience and rethinking decades-old database paradigms. They explore how foundational technologies evolve, the parallels between software and real-world systems like the electrical grid, and the emerging role of AI in coding and system design. You can connect with Yury through his personal Twitter account @1st1 (https://twitter.com/1st1) and EdgeDB's official Twitter @EdgeDatabase (https://twitter.com/edgedatabase).Check out this GPT we trained on the conversation!Timestamps00:00 Introduction to the Crazy Wisdom Podcast00:27 What is EdgeDB?00:58 The Evolution of Databases04:36 Understanding SQL and Relational Databases07:48 The Importance of Database Relationships09:27 Schema vs. No-Schema Databases14:14 EdgeDB: SQL 2.0 and Developer Experience23:09 The Future of Databases and AI Integration26:43 AI's Role in Software Development27:20 Challenges with AI-Generated Code29:56 Human-AI Collaboration in Coding34:00 Future of Programming Languages44:28 Junior Developers and AI Tools50:02 EdgeDB's Vision and Future PlansKey InsightsReimagining Relational Databases: Yury Selivanov explains how EdgeDB represents a modern rethinking of relational databases. Unlike traditional databases designed with 1970s paradigms, EdgeDB focuses on improving developer experience by introducing object-oriented schemas and hierarchical query capabilities, bridging the gap between modern programming needs and legacy systems.Bridging Data Models and Code: A key challenge in software development is the object-relational impedance mismatch, where relational database tables do not naturally map to object-based data models in programming languages. EdgeDB addresses this by providing a high-level data model and query language that aligns with how developers think and work, eliminating the need for complex ORMs.Advancing Query Language Design: Traditional SQL, while powerful, can be cumbersome for application development. EdgeDB introduces EdgeQL, a modern query language designed for readability, hierarchical data handling, and developer productivity. This new language reduces the friction of working with relational data in real-world software projects.AI as a Tool, Not a Replacement: While AI has transformed coding productivity, Yury emphasizes that it is a tool to assist, not replace, developers. LLMs like GPT can generate code, but the resulting systems still require human oversight for debugging, optimization, and long-term maintenance, highlighting the enduring importance of experienced engineers.The Role of Schema in Data Integrity: Schema-defined databases like EdgeDB allow developers to codify business logic and enforce data integrity directly within the database. This reduces the need for application-level checks, simplifying the codebase while ensuring robust data consistency—a feature that remains critical even in the era of AI.Integrating AI into Databases: EdgeDB is exploring innovative integrations of AI, such as automatic embedding generation and retrieval-augmented generation (RAG) endpoints, to enhance data usability and simplify complex workflows. These capabilities position EdgeDB as a forward-thinking tool in the rapidly evolving landscape of AI-enhanced software.Balancing Adoption and Usability: To encourage adoption, EdgeDB is incorporating familiar tools like SQL alongside its advanced features, lowering the learning curve for new users. This approach combines innovation with accessibility, ensuring that developers can transition seamlessly to the platform while benefiting from its modern capabilities.

This week we take a look at Bevy, a new game engine written in Rust. And in particular, we look at a core component of Bevy that has something to teach you even if you never write a game: its Entity Component System, or ECS. An ECS is an approach to managing complex systems with large numbers of moving parts, that takes some inspiration from the Relational Database world, and a little from Functional Programming to build something entirely unique and surprisingly high-performance.Joining us to explain all is Alice Cecile. She's part of the Bevy foundation, which is charting a course from data-management and rendering tool to fully-featured game development environment. A journey they've made huge progress on, but still expect to take another decade to come to full fruition. We look at the core ECS, and the wider project-management approaches they need to make the journey.–Support Developer Voices on Patreon: https://patreon.com/DeveloperVoicesSupport Developer Voices on YouTube: https://www.youtube.com/@developervoices/joinBevy: https://bevyengine.org/Bevy Examples: https://bevyengine.org/examples/Flecs (C++): https://github.com/SanderMertens/flecsTiny Glade (game): https://store.steampowered.com/app/2198150/Tiny_Glade/Alice on Mastodon: https://mastodon.gamedev.place/@alice_i_cecileKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkins

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    

With a nod to the late 1980's Scorpions song, let's dive into the prevailing forces that are causing organizations to reevaluate their strategies with relational databases. To do that effectively, we welcome back to the program Ryan Arsenault, Principal Field Solution Architect (and heavily accented Bostonian), who has broad experience previously running SAP and Database operations at a large competitor. Ryan and I navigate through the landscape of trends that are requiring DBAs and IT leaders to change their approach to the software that manages their most important assets - the database. We touch on the impact of Cloud, Automation, AI and AI operations, Security, and the recent changes with VMware - plus how all-Flash changed the game for tier 1 database storage management. Always a fun conversation when Ryan stops by. For more information on Pure Storage solutions for Databases, check out: https://www.purestorage.com/solutions/applications.html

edX ✨I build courses: https://insight.paiml.com/d69  

Summary Databases come in a variety of formats for different use cases. The default association with the term "database" is relational engines, but non-relational engines are also used quite widely. In this episode Oren Eini, CEO and creator of RavenDB, explores the nuances of relational vs. non-relational engines, and the strategies for designing a non-relational database. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is brought to you by Datafold – a testing automation platform for data engineers that prevents data quality issues from entering every part of your data workflow, from migration to dbt deployment. Datafold has recently launched data replication testing, providing ongoing validation for source-to-target replication. Leverage Datafold's fast cross-database data diffing and Monitoring to test your replication pipelines automatically and continuously. Validate consistency between source and target at any scale, and receive alerts about any discrepancies. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold (https://www.dataengineeringpodcast.com/datafold). Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Oren Eini about the work of designing and building a NoSQL database engine Interview Introduction How did you get involved in the area of data management? Can you describe what constitutes a NoSQL database? How have the requirements and applications of NoSQL engines changed since they first became popular ~15 years ago? What are the factors that convince teams to use a NoSQL vs. SQL database? NoSQL is a generalized term that encompasses a number of different data models. How does the underlying representation (e.g. document, K/V, graph) change that calculus? How have the evolution in data formats (e.g. N-dimensional vectors, point clouds, etc.) changed the landscape for NoSQL engines? When designing and building a database, what are the initial set of questions that need to be answered? How many "core capabilities" can you reasonably design around before they conflict with each other? How have you approached the evolution of RavenDB as you add new capabilities and mature the project? What are some of the early decisions that had to be unwound to enable new capabilities? If you were to start from scratch today, what database would you build? What are the most interesting, innovative, or unexpected ways that you have seen RavenDB/NoSQL databases used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on RavenDB? When is a NoSQL database/RavenDB the wrong choice? What do you have planned for the future of RavenDB? Contact Info Blog (https://ayende.com/blog/) LinkedIn (https://www.linkedin.com/in/ravendb/?originalSubdomain=il) 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. Links RavenDB (https://ravendb.net/) RSS (https://en.wikipedia.org/wiki/RSS) Object Relational Mapper (ORM) (https://en.wikipedia.org/wiki/Object%E2%80%93relational_mapping) Relational Database (https://en.wikipedia.org/wiki/Relational_database) NoSQL (https://en.wikipedia.org/wiki/NoSQL) CouchDB (https://couchdb.apache.org/) Navigational Database (https://en.wikipedia.org/wiki/Navigational_database) MongoDB (https://www.mongodb.com/) Redis (https://redis.io/) Neo4J (https://neo4j.com/) Cassandra (https://cassandra.apache.org/_/index.html) Column-Family (https://en.wikipedia.org/wiki/Column_family) SQLite (https://www.sqlite.org/) LevelDB (https://github.com/google/leveldb) Firebird DB (https://firebirdsql.org/) fsync (https://man7.org/linux/man-pages/man2/fsync.2.html) Esent DB? (https://learn.microsoft.com/en-us/windows/win32/extensible-storage-engine/extensible-storage-engine-managed-reference) KNN == K-Nearest Neighbors (https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) RocksDB (https://rocksdb.org/) C# Language (https://en.wikipedia.org/wiki/C_Sharp_(programming_language)) ASP.NET (https://en.wikipedia.org/wiki/ASP.NET) QUIC (https://en.wikipedia.org/wiki/QUIC) Dynamo Paper (https://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf) Database Internals (https://amzn.to/49A5wjF) book (affiliate link) Designing Data Intensive Applications (https://amzn.to/3JgCZFh) book (affiliate link) 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/)

Today we have the former CEO of Snowflake, a 23 year veteran of Microsoft, Bob Muglia on the show. In this interview, we discuss Bob's book, Datapreneurs, which takes you on a journey about the people behind the first relational databases in the 1970s and early 80s, to Bob's experience launching Microsoft SQL Server and a ton of other products, developing the Data Cloud at Snowflake, and to the future of data and AI. We cover a lot of ground, including some of his experience working alongside the likes of Bill Gates and Steve Ballmer. Timestamps: 02:24 Introduction 04:53 Relational Databases 18:43 Speed of Innovations 24:30 Keeping the Early Stage Culture 31:04 Most successful leaders are difficult to deal with 34:31 Setting up Cloud Data Center at home 36:25 Joining Snowflake as the CEO 38:54 AWS made Snowflake happen 42:18 Google, AWS Missing the Snowflake Opportunity 46:13 Impact On Jobs 50:48 Existential Risk 52:28 Staying Optimistic Links: The Datapreneurs: The Promise of AI and the Creators Building Our Future https://www.thedatapreneurs.com/ Follow Bob: https://twitter.com/Bob_Muglia Follow Sean: https://twitter.com/seanfalconer Software Huddle ⤵︎ X: https://twitter.com/SoftwareHuddle LinkedIn: https://www.linkedin.com/company/softwarehuddle/ Substack: https://softwarehuddle.substack.com/

This week we discussed Cosmos DB, a No SQL and relational database system hosted and managed by Microsoft. Cosmos DB is a cloud first service from Microsoft on Azure. It's a modern approach to a database management system, built to achieve global scale and high availability. There are many APIs to pick from: NoSQL MongoDB PostgreSQL Apache Cassandra Apache Gremlin Table Storage Sam takes the lead with providing insights such as: What is Cosmos DB and how it's different to other database management systems What APIs are supported, and how do you decide? Operational considerations of Cosmos DB What did you think of this episode? Give us some feedback via our contact form, Or leave us a voice message in the bottom right corner of our site.Read transcript

Hey Everyone, In this episode I discuss Twisp's journey with Mike Parsons, CTO and Co-Founder of Twisp. We have discussed how they ended up building a cloud native distributed ledger database on top of DynamoDB, why they chose DynamoDB? what were the challenges they faced and many highly technical and very interesting topics. Chapters: 00:00 Introduction - Twisp - CTO and Co-Founder Mike Parsons 03:01 What do we mean by a Distributed Ledger Database? 07:13 Is it similar to Blockchain? 08:32 Challenges faced with Relational Database setup 19:39 Why DynamoDB was the right choice? 24:40 What was the scale you were building for? 26:05 Who uses a Ledger database? 29:30 What are the basic primtives in a ledger database? 33:55 Is every primitive Immutable? How is it stored? 38:07 Impact of Immutability on Data storage 41:33 How does idempotency work in Twisp? 44:53 How did you provide ACID guarantees on top of DynamoDB? 52:30 How do you ensure correctness? 57:13 Interesting real world usecases where Twisp is used. 01:02:33 Are you planning to build even higher level primitives? References: Clocks: https://cse.buffalo.edu/tech-reports/... and http://rystsov.info/2018/10/01/tso.html TicToc: https://people.csail.mit.edu/sanchez/... Hekaton Paper: https://www.microsoft.com/en-us/resea... Hermitage Test suite: https://github.com/ept/hermitage Online Async Schema Change in F1 : https://static.googleusercontent.com/... Designing Data intensive Applications: https://www.oreilly.com/library/view/... P Programming language: https://github.com/p-org/P Twisp: https://www.twisp.com/ AWS Dynamo DB: https://aws.amazon.com/pm/dynamodb/?t... Connect with Mike: https://www.linkedin.com/in/parsnips/ Follow me: Twitter: https://twitter.com/thegeeknarrator Linkedin: https://www.linkedin.com/in/kaivalyaa... Watch other playlists: Databases:    • Modern Databases   Distributed systems:    • Distributed Systems   Software Engineering:    • Software Engineering   Sofk skills:    • Soft Skills   Like, share and subscribe to the channel folks. See you in the other episode. Cheers, The GeekNarrator

Howie Liu is the co-founder and CEO of tech decacorn Airtable, a connected apps platform that lets users build and customize apps using shared data. As a no code platform, Airtable has iterated and innovated their product by combining the features of a relational database by using an interface that looks like a spreadsheet. Since it's founding in 2012, the company has raised nearly $1.4B in funding and is currently valued at $11B. In this episode, Howie talks about starting his first company, taking part in the Y Combinator accelerator, and how selling his company Etacts to Salesforce led him to innovating and creating products with enterprise clients in mind. Liu is a graduate of Duke University.

InfosecTrain hosts a live event entitled ‘All About SQL' with certified expert ‘NAWAJ'. SQL is used to search through a database store, which is a set of data files. The application of SQL is generally referred to as an RDBMS (Relational Database Management System). SQL is used in various business industries including technology, banking, retail, music and even medical sectors; it is very user-friendly, scalable and accessible. SQL is open-source, and has excellent community support. This masterclass from Infosectrain will teach you all about SQL, its installation, best practices, and Relational Database. SQL Masterclass Day 1: https://youtu.be/XvcHMoNDlVQ SQL Masterclass Day 2: https://youtu.be/MrQbqyy7KXQ Thank you for watching this video, For more details or free demo with out expert write into us at sales@infosectrain.com ✅Agenda of the Session ✔️Relational Database & Introduction to SQL ✔️Installation and Get to Know My SQL ✔️Best SQL Practices ✔️SQL Query Methods Subscribe to our channel to get video updates. Hit the subscribe button above. Facebook: https://www.facebook.com/Infosectrain/ Twitter: https://twitter.com/Infosec_Train LinkedIn: https://www.linkedin.com/company/infosec-train/ Instagram: https://www.instagram.com/infosectrain/ Telegram: https://t.me/infosectrains

InfosecTrain hosts a live event entitled ‘All About SQL' with certified expert ‘NAWAJ'. SQL is used to search through a database store, which is a set of data files. The application of SQL is generally referred to as an RDBMS (Relational Database Management System). SQL is used in various business industries including technology, banking, retail, music and even medical sectors; it is very user-friendly, scalable and accessible. SQL is open-source, and has excellent community support. This masterclass from Infosectrain will teach you all about SQL, its installation, best practices, and Relational Database. SQL Masterclass Day 1: https://youtu.be/XvcHMoNDlVQ SQL Masterclass Day 3: https://youtu.be/UzoZgctfWsw Thank you for watching this video, For more details or free demo with out expert write into us at sales@infosectrain.com ✅Agenda of the Session ✔️Relational Database & Introduction to SQL ✔️Installation and Get to Know My SQL ✔️Best SQL Practices ✔️SQL Query Methods Subscribe to our channel to get video updates. Hit the subscribe button above. Facebook: https://www.facebook.com/Infosectrain/ Twitter: https://twitter.com/Infosec_Train LinkedIn: https://www.linkedin.com/company/infosec-train/ Instagram: https://www.instagram.com/infosectrain/ Telegram: https://t.me/infosectrains

InfosecTrain hosts a live event entitled ‘All About SQL' with certified expert ‘NAWAJ'. SQL is used to search through a database store, which is a set of data files. The application of SQL is generally referred to as an RDBMS (Relational Database Management System). SQL is used in various business industries including technology, banking, retail, music and even medical sectors; it is very user-friendly, scalable and accessible. SQL is open-source, and has excellent community support. This masterclass from Infosectrain will teach you all about SQL, its installation, best practices, and Relational Database. SQL Masterclass Day 2: https://youtu.be/MrQbqyy7KXQ SQL Masterclass Day 3: https://youtu.be/UzoZgctfWsw Thank you for watching this video, For more details or free demo with out expert write into us at sales@infosectrain.com ✅Agenda of the Session ✔️Relational Database & Introduction to SQL ✔️Installation and Get to Know My SQL ✔️Best SQL Practices ✔️SQL Query Methods Subscribe to our channel to get video updates. Hit the subscribe button above. Facebook: https://www.facebook.com/Infosectrain/ Twitter: https://twitter.com/Infosec_Train LinkedIn: https://www.linkedin.com/company/infosec-train/ Instagram: https://www.instagram.com/infosectrain/ Telegram: https://t.me/infosectrains

Listen to Intricity Data Sharks: https://overcast.fm/+x8z_QG3XM

John and Rambo share their initial impressions of some of the new APIs that were announced at WWDC22, and talk about managing releases, learning when and how to apply generics, and the launch of the Gigahertz podcast network.

An open talk on the challenges of having a data pipeline for the images, audio and videos, The Hub enables to have several famous machine learning datasets with just a single command, like CIFAR-10, MNIST or Fashion-MNIST, Google Objection, ImageNet, COCO, and many others. As I came from a Relational Database management system (RDBS) background, this talk gives me a new perspective and helps to think outside of the known areas. Enjoy the talk with the CEO of Active Loop. This session was recorded in October 2021 and is now being published. Today's Guest Davit Buniatyan, CEO at ActiveLoop.ai A great insight talk with the guest speaker on a topic, a product owner focuses on a dataset format to offer API for creating, storing, and collaborating on any size of AI datasets. What were the challenges faced in the unstructured data storage and how the hub is offering a solution to solve the data problem? The question of where you will store the large data sets of images, and videos - you will get all the answers in this talk How the opensource Github repo is helping thousands of people to use datasets to PyTorch or TensorFlow with one line of code. Website: ActiveLoop.ai Twitter: activeloop Resources: ActiveLoop hub - dataset for AI Book: Good to Great

Distributed SQL (also called New SQL) aims to modernize relational databases by bringing better availability, scale, and performance through a cloud-native approach.  Yugabyte is one of the leaders at the forefront of this movement by marrying the rock solid PostgreSQL client & protocol with a brand new cloud-native backend.  Denis Magda stops by and chat's with the HOSS not only about Yugabyte but also about his work on various Apache projects.

On today's podcast we chat about what they are, why we use them, and specific examples to help you understand the benefit of using relational databases.

On this week's episode, Chris and Steph discuss migrating a polymorphic relationship over to UUIDs and balancing trade-offs between data integrity vs complexity. They also touch on a new Rails feature that adds support to safely remove and add columns, GitHub Discussions, measuring team experiments, and purposeful communication. This episode is brought to you by ScoutAPM (https://scoutapm.com/bikeshed). Give Scout a try for free today and Scout will donate $5 to the open source project of your choice when you deploy Modeling Polymorphic Associations in a Relational Database (https://hashrocket.com/blog/posts/modeling-polymorphic-associations-in-a-relational-database) Rails 6.1 - Add support for ifexists/ifnot_exists (https://github.com/rails/rails/pull/38352) GitHub Discussions (https://docs.github.com/en/discussions) Loom: Video Messaging (https://www.loom.com/)

Javascript developers rejoice! Amy Tom talks to Eric Bishard and Arun Vijayaraghavan about the differences between a Document Database and a Relational Database. Eric and Arun explain JSON Document Databases and also discuss NodeJS SDKs, ODMs vs ORMs, and more. Eric is the Developer Advocate and Arun is a Product Manager; both work at Couchbase. In this episode, Amy chats to Eric and Arun about: The differences between a Document Database and a Relational Database (06:56) The differences between an ODM and an ORM (18:45) Learning Nickel Querying through Ottoman ODM (26:58) Follow Arun Vijayaraghavan and Eric Bishard: Follow Arun Vijayaraghavan on GitHub Connect with Arun Vijayaraghavan on LinkedIn Follow Eric Bishard on Twitter Follow Eric Bishard on GitHub Follow Eric Bishard on Dev.to Read more on Hacker Noon: Eric Bishard's Introduction to Ottoman on Hacker Noon Shownotes: Developer.couchbase.com A JSON Data Modeling Guide Ottoman Documentation

שלום וברוכים הבאים לפרק מספר 405 של רברס עם פלטרפורמה - באמפרס מספר 68 - והתאריך היום הוא ה-1 באפריל 2021.היום יש לנו אורח מיוחד - תגידו שלום למיסטר טרנס אקשן (Terence Action)!(רן) שלום מר טרנס(טרנס) אהלן, בוקר טוב, מה קורה?(רן) בוקר טוב, אנחנו שמחים שאתה איתנו - ואיתנו גם אלון ודותן, הי חברים.אז - מר טרנס, קודם כל תודה שאתה זורם איתנו עם העברית… יש לך רקע עשיר ומגוון בכל מה שקשור בביזור של Databases, וגם אחרים . . . בוא, ספר לנו קצת על הרקע שלך, מאיפה אתה מגיע?(טרנס) תראה, הכל התחיל בשנות ה-60, כשהתחלתי את המאסטר שלי, ואני וקנות’ ישבנו ודיברנו על מחשביםקנות’ אמר . . .(רן) אתה מתכוון ל - Donald Knuth, נכון?(טרנס) כן, אתה יודע . . . מצטער שאני עושה Name-dropping אבל ככה . . . חבר יקר.יום אחד הוא קם ופתח עיניים גדולות, כמו צלחות, ואמר “יום אחד מחשבים יעבדו, כאילו - הרבה ביחד, הרבה הרבה הרבה ביחד” . . . אמרתי לו “וואו!”, והלכתי ולקחתי את זה איתי הביתה, וישבתי בבית וחשבתי לי, קצת עשיתי אסיד, ואמרתי לעצמי “וואו - אם הם עובדים הרבה ביחד, אז לפעמים חלק ייפלו!”ואז ידעתי מה יהיה המאסטר שלי, ה-Citation . . . וכתבתי על מערכות מבוזרות.(רן) אתה רוצה להגיד לי שאתה קודם כתבת את ה-Master, ורק אחר כך את ה-Slaves, באוניברסיטה?(טרנס) וואו, אתה מנסה להפיל אותי פה עם כל העברית החדשה הזו, הא? אני כתבתי את ה-Leading version, ואחר כך את ה-Clone, כן . . (רן) היית Leader, כן . . . אז באילו Databases יצא לך לעסוק בחיים המקצועיים שלך?(טרנס) אז תראה - כמו כולם, התחלתי ב-Sybase, ואחר זה הכנסתי את SQLite לעבודה ב-!Yahoo . . .כל המייל של !Yahoo ישב על SQLite, לא רבים יודעים את זה . . . המנגנון Anti-spam היה בעצם Select למילים, כמו “ציצים” וכאלה, בהתחלה - ואחרי שמנו שם כל מיני בינה מאלכותית.ואז, כשהיינו כבר בקבצים של ה-SQLite, אמרתי “מה יקרה אם נשבור אותם לפי משהו? לפי נושאים, אות ראשונה של המייל? לפי משהו” . . . ואז, ככה, המצאתי בעצם את ה-Sharding . . . זה היה . . .(אלון) ה-Sharding הראשון שלך היה בעצם על Distributed Access DB, נכון?(טרנס) כן - זה היה חתיכת סיפור, כי Access DB הוא בפורמט בינארי, ואני לא יודע אם אתה מכיר פורמטים בינאריים, אבל הם טיפה’לה יותר מסובכים מפורמטים לא-בינאריים, טקסטואליים . . . ואז הייתה בעיה מאוד קשה ב-Access - אי אפשר היה לעשות בשאילתה אחת גם Min, גם Max וגם אגרגציה (Aggregation) - אז ישבתי ושברתי את זה לשלושה Access DBs, בשלושה Access DB האלה - על אחד עשיתי Max, על אחד עשיתי Min ועל השלישי עשיתי אגרגציה.ואז המצאתי משהו חדש, שכאילו מאחד את כל התוצאות ביחד - כאילו אתה לא הרגשת את זה.(דותן) אבל למה לך לעשות Max ו-Min? זה לא ש-Min זה הפוך של Max ו-Max זה הפוך של Min? לא עושים 1- . . .?(טרנס) אז לא קראו לזה Product, אז קראו לו Stephen . . . סטפן אמר שצריך Max ו-Minניסיתי להתווכח אבל אתה יודע - Product היו קשוחים, אז אם הוא רוצה Max ו-Min אז ניתן לו Max ו-Min, מה אני אעשה? . . .(רן) אבל יש את ה . . . זה לא כמו ב-CAP Theorem, שיש לך Max, Min ו-Average - תבחר שנייים? אתה באמת יכול לעשות את שלושתם יחד באותה השאילתה?(טרנס) תשמע, אני לא יודע קורה עם ה-CAP Theorem הזה, בעיני זה הכל פיקציה . . .אני המצאתי את ה-Glasses Theory - זה כמו CAP, אבל כאילו שקוף - וכשהכל שקוף אז הכל גם עובד ביחדאין לך שום בעיה לעשות Through and Through Consistency ו-High Availability - וגם אם ה-Network Partition קורה, אז לא קרה כלום.(רן) אגב שקיפות - במאמרים האחרונים שלך, שאתה מפרסם, לקחת את כל הנסיון שלך ב-Databases ועשית לו Apply לקורונה, As-is . . . בוא, ספר לנו קצת על הניסיון שלך בעבודה עם צוותים מבוזרים בתקופות הקורונה.(טרנס) תשמע, קודם כל, כשהתחיל משבר הקורונה, נזכרתי במשפט שאמר לי פעם Andreessen . . שהוא אמר “במקום שבו אתה רואה משבר, אני רואה הזדמנות”.אז אמרתי מה בעצם הבעיה פה? אז אנשים מתים . . . עכשיו - אנשים מתים זה עצוב וכזה, אל תבין אותי לא נכון, אני ברגישות - אבל מה קורה אם מת לך מפתח באמצע ה-Release ,כאילו? יום לפני ה-Alpha או משהו, מה קורה?[היו מקרים גם ב-2019 . . .]אז אמרתי - וואי, מה יקרה אם נעשה Highly-redundant מפתח, אוקיי?בעצם, כשאתה לוקח מפתחים מאצלי, אז הם באים תמיד בזוגות, אבל מרחוק, ב-Remote - ואם במקרה אחד מהם מת מקורונה, אני עושה Hot-swap ומביא במקומו אחד אחר.עכשיו - מפתחים זה סתם Resources, מה הבעיה? לכתוב X זה קשה? אז הוא ישר נכנס וממשיך מאיפה שהקודם נעצר.(רן) ואם אחד מהם עושה לך NFT על הקוד? מה אתה עושה עם זה אז? לכל אחד יש את המפתחות?(טרנס) מה זאת אומרת “אם”? מה זאת אומרת . . .(רן) הם עושים את זה כבר?(טרנס) אתה קראת את האתר שלנו, הא חמוד? . . . אתה בסדר אתה.תראה - חברות ששמות אנשים בחברות, יש להן תמיד בעיה, יש בעיה במודל . . . לא יודע אם עבדת פעם עם חברת Outsourcing, אבל יש בעיה במודל - אם ה-Outsourcer הוא מצויין, אז אתה רוצה שהוא יעבור אליך, אתה לוקח אותו להיות עובדואז הוא עוזב את החברת Outsourcing ונגמר הכסף מה-Outsourcer הזהואם הוא בלאי, אז אתה תעיף את החברת Outsourcing קומפלט, לא תיקח אותם יותר, נכון? כשאתם עושים עם הראש לא שומעים את זה . . . [גם קשה לקרוא את זה . . . ](רן, אלון, דותן) נכון!(טרנס) אז אני אמרתי “קחו את העובדים בחינם - עלי - אבל הקוד שהם כותבים סגור ב-NFT” . . . אתם מכירים NFT? צריך להסביר?(דותן) לא, תסביר . . .(טרנס) אז NFT זה Crypto-Cyber-Distributed, שסוגר לך בעצם Assets דיגיטליים בצורה שבה בכל שימוש חוזר, היוצר המקורי מקבל חתיכה מהכסף.אז נגיד עכשיו, בחברה שעבדנו, נתתי להם את כל הקוד חינם, חוץ מלולאות If מ-1 עד 10 . . . הלולאות If מ-1 עד 10 סגורות ב-NFT שלי.ברגע שאתה עושה Code reuse ללולאות If מ-1 עד 10, אני מקבל ככה כמה סנטים על כל שימוש.(דותן) זה אומרת שאם יש לי מערכים קטנים אז אני בסדר, זה חינם . . .(טרנס) למי אין מערכים קטנים, תגיד לי? בחייאת . . .זה במיוחד שימושי ל-Columnar Databases . . . (דותן) אבל מה תעשה אם אנשים לוקחים מערך של 100 ומחלקים אותו למאה מערכים של 1? . . . (טרנס) תראה, הפתרון הוא לא מושלם, אבל בדיוק עכשיו המהנדסים שלי עובדים על לתפוס את ה-Until ואת ה-While - וברגע שתפסתי את ה-Until ואת ה-While, אחי - כל ה-Python של העולם יושב לי בכיס הקטן.זהו - אני סגור, אני יוצא לפנסיה, דבר איתי ב-Puerto Vallarta על מרגריטה . . .(אלון) שמע, זה אחלה מודל עסקי - הוא מחלק קוד חינם, וגוזר קופון על השימוש בלולאה . . .(דותן) נכון . . .(אלון) איך לא חשבו על זה קודם . . . (טרנס)לא רק בלולאות, לא רק בלולאות! אתה משתמש ב-Slack? (אלון) כן, אבל רק כל יום . . . (טרנס) בסדר, מעולה - אתה השתמשת פעם באייקון הזה של האיש עם השפם והמשקפיים והאף הגדול? של ה”היי, אני Nerd אבל אני מצחיקי”?(אלון) כן, ברור(טרנס) בכל פעם שאתה משתמש בו, Slack נותנים לי רבע-סנט . . . (אלון) מה אתה אומר? . . .(טרנס) סגרתי Assets מימין ומשאל, אתה לא מבין מה זה . . .(דותן) אפשר לשאול אותך, אם זה בסדר, על המקומות שבהם עבדת? שכולנו מכירים, שהם מפורסמים . . .(טרנס) עכשיו אתה מביך אותי קצת . . . אני לא רוצה להתהדר, אבל כל ה Data Consistency של Mongo? זה אני(רן) מה אתה אומר? . . . כל ה-Data Consistency של של Mongo זה אתה? פשש . . . איזו עבודה . . .(טרנס) כן, באמת, אני . . . באלפות (Alpha) הראשונות הייתי כותב במחברת, אבל אחר כך פיתחנו די הרבה קוד סביב זה.שם זה לא NFT לצערי, כי הרבה משתמשים.(אלון) וכל נושא ה-Data Loss, ב-Mongo - זה נכנס ב-Design? אחרי זה? מתי הכנסתם את הפיצ’רים?(טרנס) הכל מבוסס של AIהבטחנו שזה Eventually Consistent, אבל לא אמרנו מתי זה “Eventually” . . . עכשיו, בזמן שאתה כותב קוד, כותב Data ל-Mongo - אנחנו מג’נרטים (Generate) ספרות בפאי (

We meet Magnus Hagander who is the President of the board for PostgreSQL Europe. PostgreSQL is an open source object-relational database system. We talk about the importance of relational databases and the work within the developer community. Magnus works at Redpill Linpro in Stockholm, with support and training services, as well as customer development work.

Working with data is one of the most important aspects of development. This week I got an intro to relational databases, and here's what I learned. As we all know, a database is where we store data…what makes it relational is the method we use to store it: information is stored in tables, and then we relate those tables to each other by referencing unique id numbers from one table to the next. To interact with the data, we use a relational database management system (RDBMS) like MySQL, PostgreSQL, MariaDB, etc. Another option for storing data is to use a non-relational database like MongoDB or Neo4j which takes a less rigid approach to how and where data can be stored. These are also sometimes called NoSQL (not only SQL) databases and they provide flexibility when the data you're working with vary a lot in structure and content. However when the information is uniform, relational databases can be very efficient because there is not a lot of repetition, and you can easily access exactly the information you need with SQL, when you need it. With an RDBMS like MySQL, you can write queries to do everything from basic CRUD commands (create - read - update - delete/destroy) to refining searches and aggregating information. Using these queries and commands not only allows you to determine what information is displayed in your application, but it can also help with marketing decision, business development, advertising and much more. One correction—when I gave the Instagram example, I said that you would have a table for users, and then a table for “that user's photos” and so on. I meant to say you would have a table for ALL photos—it would not be a table for each user. You then link a user id to the photos table to show which user that photo belongs to. Sorry I misspoke! Show Links: Most popular baby names by US state The Ultimate MySQL Bootcamp W3 MySQL Exercises SQLZoo US Government Data UK Government Data Cloud9 web environment to practice This episode was originally published 16 January, 2018.

Let's talk about NoSQL, Firebase and my journey from Relational Databases.My contact detailsNikos' discord serverMy twitter GithubLinkedInMentorCv 

The JSON data model and the relational databse and sql model

HSBC moving from 65 relational databases to a single Global MongoDB, that might be true but it's misleading as not all systems are moved. Resources https://diginomica.com/hsbc-moves-65-relational-databases-one-global-mongodb-database Why some devs don't use MongoDB https://news.ycombinator.com/item?id=23507197 https://news.ycombinator.com/item?id=19497817 https://news.ycombinator.com/item?id=18366385 https://news.ycombinator.com/item?id=23270429 --- Send in a voice message: https://anchor.fm/hnasr/message

It seems like most developers these days are pretty comfortable with relational databases. However, not everything can (or should) fit neatly into a relational model. For various reasons, a NoSql database that stores documents may be a much better solution. Read more › The post Document VS Relational Databases appeared first on Complete Developer Podcast.

If you're wondering why GPT-3 matters and how it compares to prior efforts in this area, here is a good summary.If you want to dive deeper into the effect anxiety has on the interview process and hiring in tech, you can read up on the research here.This week's lifeboat badge goes to PerformanceDBA, who left an incredbily long and detailed answer, complete with charts and code snippets, on the following question: how to organize a relational data model for double entry accounting? 

If you're wondering why GPT-3 matters and how it compares to prior efforts in this area, here is a good summary.If you want to dive deeper into the effect anxiety has on the interview process and hiring in tech, you can read up on the research here.This week's lifeboat badge goes to PerformanceDBA, who left an incredbily long and detailed answer, complete with charts and code snippets, on the following question: how to organize a relational data model for double entry accounting? 

Game-changing talent can come from unlikely places. We sit down with Jordan, Software Developer at NC State, about his journey from business major to full-time, pro-dev whose projects save his university millions of dollars and years of development time.​

In this episode, Andrew and Fahad are looking ahead and discussing the trend toward collaboration first software. We believe the next generation of engineers, product designers, and salespeople want a real-time collaborative experience when it comes to software in the workplace and will in turn increase productivity and efficiency. Join in the conversation with us as we examine the benefits and key features of collaboration first architecture. In this episode we discuss: Software is changing to a more collaborative experience due to the remote transformation happening in almost every company Companies don't need siloed departments anymore but integrated together to provide the best possible customer service for your customersThe key features of collaboration first architecture are: working in real-time, speaking the same language across platforms, and effective communication Instead of trying to catch up to another company's collaboration methods, start thinking about the future. Consider what the next trends will be and start working to solve those potential problems. The first step leaders and companies can take toward more collaboration is to build an internal work culture that values collaboration, then look at where the handoffs happen in your business. Once you know where your company is missing communication, you can start working on integration in your existing systemsResources/Tools MentionedRemote Work is Eating the World. Will it eat you too? by Andrew WolfeThe Future of Customer Experience: Personalized, White-Glove Service for All By Rohit Agarwal, Raelyn Jacobson, Paul Kline, and Maurice ObeidThanks for listening! What did you think about this episode? Drop us a comment and let us know how we're doing. Connect with us at Skiplist.com

Graphdatenbanken sind wahre Meister vernetzter Strukturen. Eine der größten und populärsten Vertreterinnen ist Neo4j, eine in Java implementierte Open-Source-Graphdatenbank. Worin liegen die Unterschiede zu relationalen Datenbanken und welche Vorteile ergeben sich aus der Nutzung von Graphen? Das beantwortet uns Michael Hunger in Folge 61! Michael ist Lead Developer Advocate bei Neo4j und Autor verschiedener Publikationen zu diesem Thema. Dank seiner langjährigen Erfahrung mit Graphdatenbanken beantwortet uns Michael die Frage, inwiefern Graphdatenbanken eine Alternative zu relationalen Datenbanken darstellen können und wo ihr Einsatz besonders nützlich ist. Außerdem erfahrt ihr, wieso man sie auch als Beziehungsexperten unter den Datenbanken verstehen kann. Schön, dass ihr im Livestream eure Fragen eingebracht habt. Weitere Links aus der Folge findet ihr hier: Neo4j Webseite Von Michael angesprochene Downloads Neo4js Abfragesprache Cypher Neo4j auf Dockerhub Code von Neo4j auf GitHub Michael ist als @mesirii auf Twitter erreichbar. Picks of the Day Fabi: Ganz einfach mit Neo4j starten über die Sandbox Michael: Chrome-Extensions The Great Suspender zur Verbesserung der Browser-Performance Quick Tabs Drop-Down-Menü für Tabs Streamt uns! Die nächste Live-Folge nehmen wir am Mittwoch, den 20. Mai, um 18 Uhr auf. Seid dabei und bringt eure Fragen und Anregungen ein! Auch auf unserer Webseite erhaltet ihr mehr Informationen dazu. Schreibt uns! Schickt uns eure Themenwünsche und euer Feedback. podcast@programmier.bar Folgt uns! Bleibt auf dem Laufenden über zukünftige Folgen und virtuelle Meetups und beteiligt euch an Community-Diskussionen. Twitter Instagram Facebook Meetup Musik: Hanimo

In this episode, Adam is talks to Alex DeBrie about DynamoDB, and how it compares to relational databases like MySQL. Topics include: Does DynamoDB only make sense for things like your cache, or is it a good choice for a primary data store? An overview of the terminology used in DynamoDB and how the terminology compares to a relational database How primary keys work in DynamoDB What data types are available in DynamoDB How DynamoDB is a schemaless database Why it's important to understand your access patterns in advance with DynamoDB, unlike in a relational database Understanding why and how you usually have multiple record types in a single DynamoDB table What "index overloading" is in DynamoDB Understanding partition keys and sort keys How to structure your data in DynamoDB to make it possible to query related data, and how those queries work How secondary indexes work, allowing you to access the same data in different ways How to accommodate access patterns you didn't know about before you designed your schema When to flatten relationships vs. nest them Should you use DynamoDB if you aren't "web-scale"? How local development works with DynamoDB Links: DynamoDB Homepage Alex's blog, loaded with great DynamoDB content The DynamoDB Book, Alex's recent book DynamoDB Guide Supporting the show: I decided to stop taking sponsors for the show because I think advertisements are annoying and no one wants to listen to them. If you do want to support the show, the best way to do it is to purchase one of my products: Tailwind UI, a collection of professionally designed, fully responsive HTML components built with Tailwind CSS Refactoring UI, a book and video series I put together with Steve Schoger on designing beautiful user interfaces, without relying on a designer. Advanced Vue Component Design, a course on designing simpler, more flexible Vue components that are both more powerful and easier to maintain. Test-Driven Laravel, a massive video course on designing robust Laravel applications with TDD. Learn how to build a real-world application from scratch without writing a single line of untested code. Refactoring to Collections, a book and video course that teaches you how to apply functional programming principles to break down ugly, complex code into simple transformations — free of loops, complex conditionals, and temporary variables.

Distributed SQL Query Engine.Presto is an open-source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. Presto allows querying data where it lives, including Hive, Cassandra, relational databases, or even proprietary data stores. A single Presto query can combine data from multiple sources, allowing for analytics across your entire organization. Hosts: Randal Schwartz and Simon Phipps Guests: Manfred Moser and Dain Sundstrom Download or subscribe to this show at https://twit.tv/shows/floss-weekly Here's what's coming up for FLOSS in the future. Think your open source project should be on FLOSS Weekly? Email Randal at merlyn@stonehenge.com Thanks to Lullabot's Jeff Robbins, web designer and musician, for our theme music. Sponsors: twit.cachefly.com barracuda.com/floss

Distributed SQL Query Engine.Presto is an open-source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. Presto allows querying data where it lives, including Hive, Cassandra, relational databases, or even proprietary data stores. A single Presto query can combine data from multiple sources, allowing for analytics across your entire organization. Hosts: Randal Schwartz and Simon Phipps Guests: Manfred Moser and Dain Sundstrom Download or subscribe to this show at https://twit.tv/shows/floss-weekly Here's what's coming up for FLOSS in the future. Think your open source project should be on FLOSS Weekly? Email Randal at merlyn@stonehenge.com Thanks to Lullabot's Jeff Robbins, web designer and musician, for our theme music. Sponsors: twit.cachefly.com barracuda.com/floss

Distributed SQL Query Engine.Presto is an open-source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. Presto allows querying data where it lives, including Hive, Cassandra, relational databases, or even proprietary data stores. A single Presto query can combine data from multiple sources, allowing for analytics across your entire organization. Hosts: Randal Schwartz and Simon Phipps Guests: Manfred Moser and Dain Sundstrom Download or subscribe to this show at https://twit.tv/shows/floss-weekly Here's what's coming up for FLOSS in the future. Think your open source project should be on FLOSS Weekly? Email Randal at merlyn@stonehenge.com Thanks to Lullabot's Jeff Robbins, web designer and musician, for our theme music. Sponsors: twit.cachefly.com barracuda.com/floss

Distributed SQL Query Engine.Presto is an open-source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. Presto allows querying data where it lives, including Hive, Cassandra, relational databases, or even proprietary data stores. A single Presto query can combine data from multiple sources, allowing for analytics across your entire organization. Hosts: Randal Schwartz and Simon Phipps Guests: Manfred Moser and Dain Sundstrom Download or subscribe to this show at https://twit.tv/shows/floss-weekly Here's what's coming up for FLOSS in the future. Think your open source project should be on FLOSS Weekly? Email Randal at merlyn@stonehenge.com Thanks to Lullabot's Jeff Robbins, web designer and musician, for our theme music. Sponsors: twit.cachefly.com barracuda.com/floss

Distributed SQL Query Engine.Presto is an open-source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. Presto allows querying data where it lives, including Hive, Cassandra, relational databases, or even proprietary data stores. A single Presto query can combine data from multiple sources, allowing for analytics across your entire organization. Hosts: Randal Schwartz and Simon Phipps Guests: Manfred Moser and Dain Sundstrom Download or subscribe to this show at https://twit.tv/shows/floss-weekly Here's what's coming up for FLOSS in the future. Think your open source project should be on FLOSS Weekly? Email Randal at merlyn@stonehenge.com Thanks to Lullabot's Jeff Robbins, web designer and musician, for our theme music. Sponsors: twit.cachefly.com barracuda.com/floss

Distributed SQL Query Engine.Presto is an open-source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. Presto allows querying data where it lives, including Hive, Cassandra, relational databases, or even proprietary data stores. A single Presto query can combine data from multiple sources, allowing for analytics across your entire organization. Hosts: Randal Schwartz and Simon Phipps Guests: Manfred Moser and Dain Sundstrom Download or subscribe to this show at https://twit.tv/shows/floss-weekly Here's what's coming up for FLOSS in the future. Think your open source project should be on FLOSS Weekly? Email Randal at merlyn@stonehenge.com Thanks to Lullabot's Jeff Robbins, web designer and musician, for our theme music. Sponsors: twit.cachefly.com barracuda.com/floss

In this episode, we cover the following topics: In this new series, we are discussing database consistency models explained in three acts. This episode is "Act I: Transaction processing (circa 1973)". We start with the motivation behind talking about database soup - why are ACID, CAP, and BASE important to understand? We define transaction processing and its origins. What exactly is a "transaction"? Transactions are governed by ACID semantics. We define and explain the four characteristics of the ACID acronym:: Atomicity Consistency Isolation Durability The computer scientist, Jim Gray, came up with the idea of ACID semantics in the late 1970's. We discuss some of the history behind this, along with a bizarre and tragic ending to his story. We also share a personal story about another important player in transaction processing, Phil Bernstein. Detailed Show NotesWant the complete episode outline with detailed notes? Sign up here: https://mobycast.fm/show-notes/Support Mobycasthttps://glow.fm/mobycastEnd SongTalcum by Lost LakeMore InfoFor a full transcription of this episode, please visit the episode webpage.We'd love to hear from you! You can reach us at: Web: https://mobycast.fm Voicemail: 844-818-0993 Email: ask@mobycast.fm Twitter: https://twitter.com/hashtag/mobycast Reddit: https://reddit.com/r/mobycast

Jay Kreps (Co-creator of Apache Kafka® and CEO, Confluent) introduces ksqlDB, an event streaming database. As the successor to KSQL, ksqlDB seeks to unify the multiple systems involved in stream processing into a single, easy-to-use solution for building event streaming applications.ksqlDB offers support for running connectors in an embedded mode, in addition to support for both push and pull queries. Push queries allow you to subscribe to changing query results as new events occur, while pull queries allow you to look up a particular value at a single point in time. To use a ride-sharing app as an example, there is both a continuous feed of the current position of the driver (a push query) and the ability to look up current values such as the price of the ride (a pull query). Databases are still effective in their own realms, and ksqlDB is not intended as a replacement. Rather, ksqlDB enables you to build event streaming applications with the same ease and familiarity of building traditional applications on a relational database. It simplifies the underlying architecture for these applications so you can build powerful, real-time systems with just a few SQL statements.EPISODE LINKSLearn about ksqlDB on the blogWatch the demo to see ksqlDB in actionGet started with ksqlDBFollow ksqlDB on TwitterWhy Kafka Connect? ft. Robin MoffattContributing to Open Source with the Kafka Connect MongoDB Sink ft. Hans-Peter GrahslConnecting to Apache Kafka with Neo4jJoin the Confluent Community SlackFully managed Apache Kafka as a service! Try free.

W. Curtis Preston (Mr. Backup) and Prasanna Malaiyandi discuss the various ways one can backup a relational database, like Oracle, SQL Server, DB2, MySQL, and PostgreSQL.

  ACID ACID are four properties of relational database, they Atomocity, consistency, isolation and durability, and I think any one working with a relational database like postgres, mysql, sqlserver oracle, should understand these properties. In this video we will go through the four properties and explain why each is critical to make a relational database we will also talk about why some people are moving to  NOSQL database Atomicity  All or none. if a failure happened during transaction, db failure, or one of the queries failed.  Example Isolation Concurrency, is transaction isolated from other inflight transactions? if a transaction is in flight does it see changes from other inflight transactions? Does is it see any changes? Does it only see committed changes. Does leading to inconsistent results.  Problems arising from isolation (read phenomenons)  dirty reads Non repeatable reads Phantom reads  Isolation levels Read uncommitted Read committed  Repeatable read  Serializable  Durability When I commit a transaction does my changes stay durable after the database restarts/crashes etc.  See if your data still there.  Consistency Consistency from referential integrity keys  Does the number of likes on a picture = the number of rows that the picture got on another table? If a delete a picture does all the likes of that pictures go away on the other table. Consistency in reads  If I committed something does everybody see it immediately or are they going to get an old value? Consistency in concurrency Is the view of a transaction in flight consistent? Are other inflight transactions making changes to the database affects that transaction view? Jump Codes 2:00 What is a Transaction? 4:30 Atomicity  7:00 Isolation * 9:30 Isolation - Read phenomena * 11:40 Dirty Reads 14:40 Non-repeatable Read 17:00 Phantom read 18:53 Isolation Levels*2 19:20 Read uncommitted 19:55 Read committed 21:05 Non-repeatable Read 23:40 Serializability  25:00 Isolation Levels vs Read phenomena Table 27:45 Consistency  28:30 Consistency in Data 33:50 Consistency in Reads 35:00 Eventual Consistency  40:30 Durability  Cards 27:40 Exclusive lock vs shared lock --- Send in a voice message: https://anchor.fm/hnasr/message

Datenbanken: relational oder nicht-relational? SQL oder NoSQL? Und spricht man es "Es Kuh El" oder "Sequel"? In Folge 33 sprechen wir nicht nur über unser neues Büro, sondern vor allem über Datenbanken. Wie geben einen Einblick in die von uns genutzten Technologien und erzählen von unseren Erfahrungen in Hinblick auf Skalierung und Wartung. Picks of the Day Fabi: "docker-compose start/stop/restart {service-name}": Damit muss man nicht alle Container per up und down im Fehlerfall neustarten, sondern kann es auch mit einzelnen Containern aus der docker-compose.yml machen. Dennis: Intergrieren von mehreren Kalendern in Einem. Schreibt uns! Schickt uns eure Themenwünsche und euer Feedback. podcast@programmier.bar Folgt uns! Bleibt auf dem Laufenden über zukünftige Folgen und Meetups und beteiligt euch an Community-Diskussionen. Twitter Instagram Facebook Meetup YouTube Erfahrt hier, wann das nächste Meetup in unserem Office in Bad Nauheim stattfindet. Meetup Musik: Hanimo

In this themed episode of AWS TechChat, Shane and Gabe talk about the modern relational database built for the cloud: Amazon Aurora. So prepare to SELECT some Amazon Aurora knowledge INTO your brain! They start the episode with some level setting. Amazon Aurora is a fully-managed MySQL and PostgreSQL-compatible database, purpose-built for the cloud. It has great performance, and it gives enterprise-grade reliability at 1/10th the cost of traditional options. It has a distributed, fault-tolerant, self-healing storage system that auto-scales up to 64TB. Keeping it modern and real, they then discuss Amazon Aurora Serverless, which is an on-demand, auto-scaling configuration for Amazon Aurora where the database will automatically start up, shut down, and scale capacity up or down based on your app's needs. It enables you to run your database with all the benefits that serverless brings. Finally, they close the show out with a discussion around Amazon Aurora Global Database, which is designed for globally distributed applications, allowing a single Amazon Aurora database to span multiple AWS regions. It replicates your data with no impact to database performance, enables fast local reads with low latency in each region, and provides disaster recovery from region-wide outages. Resources: • AWS Innovate 2019 Global Edition https://aws.amazon.com/events/aws-innovate/ • Amazon CloudFront https://aws.amazon.com/cloudfront/ • Lambda@Edge https://aws.amazon.com/lambda/edge/ • AWS Aurora https://aws.amazon.com/rds/aurora/ • Amazon Aurora Serverless https://aws.amazon.com/rds/aurora/serverless/ • Amazon Aurora Global Database https://aws.amazon.com/rds/aurora/global-database/

In the world of relational databases, structured query language, or SQL, has long been King of the Queries, primarily because of its ubiquity and unparalleled performance. But many users prefer a mix of imperative programming, along with declarative SQL, because its user-defined functions (or UDFs) allow for good software engineering practices like modularity, readability and re-usability. Sadly, these benefits have traditionally come with a huge performance penalty, rendering them impractical in most situations. That bothered Dr. Karthik Ramachandra, a Senior Applied Scientist at Microsoft Research India, so he’s spent a great deal of his career working on improving an imperative complement to SQL in database systems. Today, Dr. Ramachandra gives us an overview of the historic trade-offs between declarative and imperative programming paradigms, tells us some fantastic stories, including The Tale of Two Engineers and The UDF Story, Parts 1 and 2, and introduces us to Froid – that’s F-R-O-I-D, not the Austrian psychoanalyst – which is an extensible, language-agnostic framework for optimizing imperative functions in databases, offering the benefits of UDFs without sacrificing performance.

50. Hey Data, Are We Related? Relational Databases Part 2 Intended Audience: Newcomers to Pharmacy InformaticsOn today's episode, we go over more detail on relational databases, particularly on how data is stored and what type of data is out there and how it's expressed in relational databases. Some terms we go over are characters, strings, primary keys, alternate keys, foreign keys, one-to-one, one-to-many, many-to-many, null, indexes, and a lot more.Follow us on social media! Twitter: @pharmacyitme Instagram: @pharmacyinformatics Website: Pharmacy IT & Me Email: tony@pharmacyitme.com Follow Tony's personal Twitter account at @tonydaopharmd Network with other pharmacists at Pharmacists Connect!http://pharmacistsconnect.com For more information on pharmacy informatics, check out some of the following useful links: ASHP's Section of Pharmacy Informatics and Technology: https://www.ashp.org/Pharmacy-Informaticist/Section-of-Pharmacy-Informatics-and-Technology/ HIMSS Pharmacy Informatics Community: https://www.himss.org/library/pharmacy-informatics Disclaimer: Views expressed are my own and do not reflect thoughts and opinions of any entity with which I have been, am now, or will be affiliated.This podcast is powered by Pinecast.

32. Hey Data, Are We Related? Relational Databases Part 1 Intended Audience: Newcomers Today's episode focuses on a very basic introduction on what databases, tables, records, and fields are. Knowing these are essential to eventually understanding how relational databases work. More information: What are Relational Databases? https://searchdatamanagement.techtarget.com/definition/relational-database SQL Training: https://www.w3schools.com/sql/Follow us on social media! Twitter: @pharmacyitme Instagram: @pharmacyinformatics Website: Pharmacy IT & Me Email: tony@pharmacyitme.com Follow Tony's personal Twitter account at @tonydaopharmd Network with other pharmacists at Pharmacists Connect!http://pharmacistsconnect.com For more information on pharmacy informatics, check out some of the following useful links: ASHP's Section of Pharmacy Informatics and Technology: https://www.ashp.org/Pharmacy-Informaticist/Section-of-Pharmacy-Informatics-and-Technology/ HIMSS Pharmacy Informatics Community: https://www.himss.org/library/pharmacy-informaticsThis podcast is powered by Pinecast.

Amazon Relational Database Service (Amazon RDS) is a fully managed relational database service that enables you to launch an optimally configured, secure, and highly available database with just a few clicks. It manages time-consuming database administration tasks, freeing you to focus on your applications and business. We review the capabilities of the service and review the latest available featurese.

Simon takes you through a great list of new services, functions and capabilities - hopefully something for everyone! Shownotes: AWS Global Infrastructure: https://aws.amazon.com/about-aws/global-infrastructure/ Amazon EFS Now Supports Provisioned Throughput | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-efs-now-supports-provisioned-throughput/ Amazon EFS Achieves PCI DSS Compliance | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-efs-achieves-pci-dss-compliance/ Amazon EC2 P3 instances, one of the most powerful GPU instances in the cloud, now available in 6 additional regions | https://aws.amazon.com/about-aws/whats-new/2018/08/amazon-ec2-p3-instances-now-available-in-6-additional-regions/ New SBE1 Amazon EC2 instances for AWS Snowball Edge | https://aws.amazon.com/about-aws/whats-new/2018/07/new-sbe1-instances-for-snowball-edge/ Introducing Amazon EC2 R5 Instances, the next generation of memory-optimized instances | https://aws.amazon.com/about-aws/whats-new/2018/07/introducing-amazon-ec2-r5-instances/ Introducing Amazon EC2 z1d Instances with a sustained all core frequency of up to 4.0 GHz | https://aws.amazon.com/about-aws/whats-new/2018/07/introducing-amazon-ec2-z1d-instances/ Amazon EC2 M5d Instances are Now Available in Additional Regions | https://aws.amazon.com/about-aws/whats-new/2018/08/amazon-ec2-m5d-instances-are-now-available-in-additional-regions/ Amazon EC2 C5d Instances are Now Available in Additional Regions | https://aws.amazon.com/about-aws/whats-new/2018/08/amazon-ec2-c5d-instances-are-now-available-in-additional-regions/ Amazon EC2 F1 Instances Adds New Features and Performance Improvements | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-ec2-f1-instances-adds-new-features-and-performance-improvements/ Amazon EC2 Fleet Now Supports Two New Allocation Strategies: On-Demand Prioritized List, and Lowest Price | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-ec2-fleet-now-supports-two-new-allocation-strategies/ Amazon EC2 Nitro System Based Instances Now Support Faster Amazon EBS-Optimized Instance Performance | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-ec2-nitro-system-based-instances-now-support-faster-ebs-optimized-performance/ Access Reserved Instance (RI) Purchase Recommendations for your Amazon Redshift, Amazon ElastiCache, and Amazon Elasticsearch Reservations using AWS Cost Explorer | https://aws.amazon.com/about-aws/whats-new/2018/07/reserved-instance-purchase-recommendations-redshift-elasticache-elasticsearch-reservations/ AWS Systems Manager Run Command Now Streams Output to Amazon CloudWatch Logs | https://aws.amazon.com/about-aws/whats-new/2018/07/aws-systems-manager-run-command-streams-output-to-amazon-cloudwatch-logs/ AWS Systems Manager Automation Conditional Branching for Step Failure | https://aws.amazon.com/about-aws/whats-new/2018/07/aws-systems-manager-automation-conditional-branching-for-step-failure/ Amazon EKS AMI Build Scripts Available on GitHub | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-eks-ami-build-scripts-available-on-github/ Add Scaling to Services You Build on AWS | https://aws.amazon.com/about-aws/whats-new/2018/07/add-scaling-to-services-you-build-on-aws/ Announcing Bring Your Own IP for Amazon Virtual Private Cloud (Preview) | https://aws.amazon.com/about-aws/whats-new/2018/07/announcing-bring-your-own-ip-for-amazon-virtual-private-cloud-preview/ Introducing Amazon Data Lifecycle Manager for EBS Snapshots | https://aws.amazon.com/about-aws/whats-new/2018/07/introducing-amazon-data-lifecycle-manager-for-ebs-snapshots/ Amazon S3 Announces Increased Request Rate Performance | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-s3-announces-increased-request-rate-performance/ Amazon CloudFront announces four new Edge locations, including its first location in Cape Town, South Africa | https://aws.amazon.com/about-aws/whats-new/2018/07/cloudfront-capetown-launch/ Amazon CloudFront announces nine new Edge locations globally across major cities in North America, Europe, and Asia | https://aws.amazon.com/about-aws/whats-new/2018/07/cloudfront-nine-edge-locations-july2018/ Amazon Route 53 Expands Into Africa With New Edge Locations in Cape Town and Johannesburg | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-route-53-expands-into-africa-with-new-edge-locations-in-cape-town-and-johannesburg/ Amazon API Gateway Increases API Limits | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-api-gateway-increases-api-limits/ Amazon API Gateway Usage Plans Now Support Method Level Throttling | https://aws.amazon.com/about-aws/whats-new/2018/07/api-gateway-usage-plans-support-method-level-throttling/ Amazon API Gateway Supports Request/Response Parameters and Status Overrides | https://aws.amazon.com/about-aws/whats-new/2018/07/api-gateway-supports-request-response-parameters-and-status-overrides/ Automate Amazon GuardDuty Provisioning Over Multiple Accounts and Regions with AWS CloudFormation StackSets Integration | https://aws.amazon.com/about-aws/whats-new/2018/07/automate-amazon-guardduty-provisioning-over-multiple-accounts-and-regions-with-aws-cloudformation-stacksets-integration/ AWS Secrets Manager Now Supports AWS PrivateLink | https://aws.amazon.com/about-aws/whats-new/2018/07/aws-secrets-manager-now-supports-aws-privatelink/ AWS Systems Manager Parameter Store integrates with AWS Secrets Manager, and adds labeling for easy configuration updates | https://aws.amazon.com/about-aws/whats-new/2018/07/aws-systems-manager-parameter-store-integrates-with-aws-secrets-manager-and-adds-parameter-version-labeling/ Delegate Permission Management to Employees by Using IAM Permissions Boundaries | https://aws.amazon.com/about-aws/whats-new/2018/07/delegate-permission-management-to-employees-by-using-IAM-permissions-boundaries/ AWS Lambda Supports .NET Core 2.1 | https://aws.amazon.com/about-aws/whats-new/2018/06/lambda-supports-dotnetcore-twopointone/ AWS Glue now provides additional ETL job metrics | https://aws.amazon.com/about-aws/whats-new/2018/07/aws-glue-now-provides-additional-ETL-job-metrics/ AWS Glue now supports reading from Amazon DynamoDB tables | https://aws.amazon.com/about-aws/whats-new/2018/07/aws-glue-now-supports-reading-from-amazon-dynamodb-tables/ The Data Lake Solution Now Transforms and Analyzes Data | https://aws.amazon.com/about-aws/whats-new/2018/07/the-data-lake-solution-now-transforms-and-analyzes-data/ AWS Marketplace Helps Customers Quickly Map Products in Their Existing Software Inventory | https://aws.amazon.com/about-aws/whats-new/2018/07/aws-marketplace-helps-customers-quickly-map-products-in-their-existing-software-inventory/ Amazon SageMaker Now Supports Resource Tags for More Efficient Access Control | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-sagemaker-now-supports-resource-tags-for-more-efficient-access-control/ Amazon SageMaker Supports High Throughput Batch Transform Jobs for Non-Real Time Inferencing | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-sagemaker-supports-high-throughput-batch-transform-jobs-for-non-real-time-inferencing/ Amazon SageMaker Now Supports Pipe Input Mode for Built-In TensorFlow Containers | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-sagemaker-supports-pipe-input-mode-for-built-in-tensorflow-containers/ Amazon SageMaker Now Supports k-Nearest-Neighbor and Object Detection Algorithms | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-sagemaker-supports-knn-and-object-detection-algorithms/ Amazon SageMaker Announces Several Enhancements to Built-in Algorithms and Frameworks | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-sagemaker-announces-enhancements-for-built-in-algorithms-and-frameworks/ AWS Service Catalog Now Supports Service Catalog Resources in CloudFormation | https://aws.amazon.com/about-aws/whats-new/2018/07/aws-service-catalog-now-supports-service-catalog-resources-in-cloudformation/ Kinesis Video Streams now supports HTTP Live Streaming (HLS) to playback live and recorded video from devices | https://aws.amazon.com/about-aws/whats-new/2018/07/kinesis-video-adds-hls-support/ Amazon Polly Now Lets You Define the Maximum Amount of Time for Speech to Complete | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-polly-now-lets-you-define-the-maximum-amount-of-time-for-speech-to-complete/ Amazon Polly Now Supports Input Character Limit of 100K and Stores Output Files in S3 | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-polly-now-supports-input-character-limit-of-100k-and-stores-output-files-in-s3/ Amazon Polly Adds Bilingual Indian English/Hindi Language Support | https://aws.amazon.com/about-aws/whats-new/2018/08/amazon-polly-adds-bilingual-indian-english-hindi-language-support/ Amazon Translate Adds Six New Languages | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-translate-adds-six-new-languages/ Amazon Transcribe Now Lets You Designate Your Own Amazon S3 Buckets to Store Transcription Outputs | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-transcribe-now-lets-you-designate-your-own-amazon-s3-buckets-to-store-transcription-outputs/ Amazon Comprehend Now Supports Syntax Analysis | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-comprehend-now-supports-syntax-analysis/ Amazon Rekognition Increases Accuracy of Text-in-Image | https://aws.amazon.com/about-aws/whats-new/2018/08/amazon-rekognition-increases-accuracy-of-text-in-image/ AWS AppSync releases enhanced no-code GraphQL API builder, HTTP resolvers, and new built-in scalar types | https://aws.amazon.com/about-aws/whats-new/2018/07/aws-appsync-releases-enhanced-capabilities-nocode-graphql/ Introducing the Serverless Bot Framework | https://aws.amazon.com/about-aws/whats-new/2018/04/introducing-the-serverless-bot-framework/ AWS SAM CLI Launches New Commands to Simplify Testing and Debugging Serverless Applications | https://aws.amazon.com/about-aws/whats-new/2018/04/aws-sam-cli-launches-new-commands/ AWS Device Farm Adds Integration with AWS CodePipeline | https://aws.amazon.com/about-aws/whats-new/2018/07/aws-device-farm-adds-integration-with-aws-codepipeline/ Amazon Aurora Serverless Brings Serverless Computing to Relational Databases | https://aws.amazon.com/about-aws/whats-new/2018/08/amazon-aurora-serverless-brings-serverless-computing-to-relational-databases/ Amazon RDS now Provides Best Practice Recommendations | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-rds-recommendations/ Copying Amazon RDS Encrypted Snapshots across Regions now Completes Faster with Less Storage | https://aws.amazon.com/about-aws/whats-new/2018/07/rds-crossregion-incremental-encrypted-snapshots/ Amazon RDS Performance Insights on RDS for PostgreSQL | https://aws.amazon.com/about-aws/whats-new/2018/04/rds-performance-insights-on-rds-for-postgresql/ Performance Insights is Available for Amazon Aurora with MySQL Compatibility | https://aws.amazon.com/about-aws/whats-new/2018/08/performance-insights-is-available-for-amazon-aurora-with-mysql-compatibility/ Amazon DynamoDB Accelerator (DAX) SDK Enhancements | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-dynamodb-accelerator--dax--sdk-enhancements/ Amazon DynamoDB Accelerator (DAX) Adds Support for Encryption at Rest | https://aws.amazon.com/about-aws/whats-new/2018/08/amazon-dynamodb-accelerator--dax--adds-support-for-encryption-at/ Amazon DynamoDB Global Tables Now Available in Three Additional Asia Pacific Regions | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-dynamodb-global-tables-regional-expansion/ Amazon Redshift announces free upgrade for DC1 Reserved Instances to DC2 | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon_redshift_announces_free_upgrade_for_dc1_reserved_instances_to_dc2/ Amazon Redshift now provides customized best practice recommendations with Advisor | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-redshift-now-provides-customized-best-practice-recommendations-with-advisor/ Amazon Redshift now supports current and trailing tracks for release updates | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-redshift-now-supports-current-and-trailing-tracks-for-release-updates/ Amazon Redshift announces new metrics to help optimize cluster performance | https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-redshift-announces-new-metrics-to-help-optimize-cluster-performance/ Amazon Redshift announces support for lateral column alias reference | https://aws.amazon.com/about-aws/whats-new/2018/08/amazon-redshift-announces-support-for-lateral-column-alias-reference/ Amazon Redshift automatically enables short query acceleration | https://aws.amazon.com/about-aws/whats-new/2018/08/amazon-redshift-automatically-enables-short-query-acceleration/ Amazon Redshift announces support for nested data with Redshift Spectrum | https://aws.amazon.com/about-aws/whats-new/2018/08/amazon-redshift-announces-support-for-nested-data-with-redshift-spectrum/ Elastic Load Balancing Announces Support for Redirects and Fixed Responses for Application Load Balancer | https://aws.amazon.com/about-aws/whats-new/2018/07/elastic-load-balancing-announces-support-for-redirects-and-fixed-responses-for-application-load-balancer/ AWS IoT Device Defender - Now Generally Available | https://aws.amazon.com/about-aws/whats-new/2018/08/aws-iot-device-defender-now-generally-available/ AWS IoT Rules Engine Now Supports Step Functions Action | https://aws.amazon.com/about-aws/whats-new/2018/07/aws-iot-rules-engine-now-supports-step-functions-action/ Stream data 65% faster with 5x higher fan-out using new Kinesis Data Streams features | https://aws.amazon.com/about-aws/whats-new/2018/08/stream_data_65_faster_with_5x_higher_fan_out_using_new_kinesis_data_streams_features/ Amazon Elasticsearch Service now supports zero downtime, in-place version upgrades | https://aws.amazon.com/about-aws/whats-new/2018/08/amazon_elasticsearch_service_now_supports_zero_downtime_in-place_version_upgrades/ Announcing the New AWS Free Tier Widget on the AWS Billing Dashboard | https://aws.amazon.com/about-aws/whats-new/2018/07/aws-billing-dashboard-free-tier-widget/ New AWS Public Datasets Available from Allen Institute for Brain Science, NOAA, Hubble Space Telescope, and Others | https://aws.amazon.com/about-aws/whats-new/2018/07/new-aws-public-datasets-available/

Série Carreira Dev, episódio 04 - www.codenation.com.br Hey, Dev! A conversa desta semana é com a super Letícia Santos que hoje trabalha na Amazon como Software Development Engineer in Relational Database Services Team. Natural de Manaus e hoje vivendo em Seattle, EUA, ela tirou um tempinho para conversar com a gente sobre como foi sua jornada de universidade - startup - Amazon. Falamos sobre as experiências que a levaram ao cargo que se encontra hoje, o papel que o LinkedIn teve nisto e como mantém-se atualizada e constantemente em modo aprendizado. A conversa está demais - esperamos que curta tanto quanto nós! Se tiver alguma dúvida ou sugestão, aproveita para deixar nos comentários aqui embaixo, beleza? :)

Amazon RDS enables customers to launch an optimally configured, secure and highly available database with just a few clicks. It provides cost-efficient and resizable capacity while managing time-consuming database administration tasks, freeing you up to focus on your applications and business. Amazon RDS provides you six database engines to choose from, including Oracle, Microsoft SQL Server, PostgreSQL, MySQL and MariaDB. In this session, we take a closer look at the capabilities of the RDS service and review the latest features available. We do a deep dive into how RDS works and the best practices to achieve the optimal performance, flexibility, and cost saving for your databases.

In this session, Shawn Bice, VP of NoSQL and QuickSight, will cover what's new in AWS non-relational data services, such as Amazon DynamoDB, Amazon ElastiCache, and Amazon Elastisearch. We will discuss how developers might select different data services to solve different aspects of an application and demo scenarios on which application use cases lend themselves well to which data services. If you're a developer building massively scaled applications, requiring flexibility, consistent millisecond performance, and trying to understand what non-relational data service you might use, this is a great introductory session.

After a brief hiatus, we are back on the air to continue the conversation and let me tell you--we have a great conversation lined up for this episode.  The discussion around what will happen to the relational database, and by extension us as administrators continues to get quite a bit of traction.  Even within SQL Server, we are starting to see more features that don't fit the traditional relational mode and a podcast listener inquired about getting our thoughts.  As I thought about a guest for this episode, I didn't want to get someone tied to a product.  They, like me, would be biased and I wanted to get someone a bit removed from the situation. Our guest today is Andrew Snodgrass, the research vice president at Directions and we chat about the future of the relational database and what the future of the data environment we manage might look like.  I hope you will find his insights valuable as an outsider.  While we don't get into the specifics of what databases are mostly like to be around, Andrew does give us administrator some idea's on what technologies we should start exploring. What are your thoughts around the future of the relational database?  Join the conversation and let us know!

In this episode, I discuss the NoSQL database, Cassandra with industry expert, John Schulz. We explore what it is, key differences from a Relational Database, use cases and tips. The show notes can be found here: https://www.pythian.com/blog/datascape-podcast-episode-5-exploring-cassandra-database/

Amazon RDS allows customers to launch an optimally configured, secure and highly available database with just a few clicks. It provides cost-efficient and resizable capacity while managing time-consuming database administration tasks, freeing you up to focus on your applications and business. Amazon RDS provides you six database engines to choose from, including Amazon Aurora, Oracle, Microsoft SQL Server, PostgreSQL, MySQL and MariaDB. In this session, we take a closer look at the capabilities of RDS and all the different options available. We do a deep dive into how RDS works and the best practises to achive the optimal perfomance, flexibility, and cost saving for your databases.

We look at the history of database systems, learn the terminology of database systems, and review some of the common database systems that are in use.

We look at the history of database systems, learn the terminology of database systems, and review some of the common database systems that are in use.

We look at the history of database systems, learn the terminology of database systems, and review some of the common database systems that are in use.

We look at the history of database systems, learn the terminology of database systems, and review some of the common database systems that are in use.

Jon Townsend (Content Team) and Christine Parr (Relational DB Product Owner) talk about Relational Database while focusing on a. Our team is responsible for building out the database as a service product with a focus on relational database engines like: MySQL, MSSQL and Oracle. The team released its first database engine in late January of this year (2016), which is a MySQL-compatible Relational DB product.

Jon Townsend (Content Team) and Christine Parr (Relational DB Product Owner) talk about Relational Database while focusing on a. Our team is responsible for building out the database as a service product with a focus on relational database engines like: MySQL, MSSQL and Oracle. The team released its first database engine in late January of this year (2016), which is a MySQL-compatible Relational DB product.

In this episode I am joined by a company that was once using an Oracle RDBMS and has since switched entirely to using NoSQL. I am joined by Tom Coates, who is a Senior Principal Architect at Nuance. I interview Tom to find out more information regarding how they were using Oracle at Nuance and what were the driving factors in why they switched completely to a new and completely different database platform. In this episode you'll learn more about the type of data being stored, the complete transition process, and any difficulties that came up along the way. If you have questions regarding todays episode or for the guest speaker, send an email to advocates@couchbase.com. You'll either receive a response via email or have your question answered in a future episode. You can learn more about Nuance by visiting http://www.nuance.com

In this episode of #BITTechTalk, Greg and Ayori welcome Joshua Hoskins to the podcast to talk about his journey to becoming a salesforce.com consulting MVP. Joshua shares valuable tips on driving your career, taking risks, starting his own company, relationship management, life hacking, work-life balance and how to get a job offer while attending dreamforce. We even manage to get into the nitty gritty details of data modeling on cloud platforms! Tune in because won’t want to miss this podcast!About our guest:Joshua has been using computers since the age of 4 and began writing code in the 5th grade. By age 22 he was Director of IT. While being extremely passionate about Customer Relationship Management & Application Development he lives to build reliable, robust and scalable solutions just about anything you can think of.Joshua’s specialties are Force.com Development, Salesforce.com Customization/Implementation, Data / Solution Architecture, Cloud & On-Premise Integrations, Configuration Management, Project Management, Ruby on Rails, ASP, VBScript, System Administration, Relational Databases, Networking, Computer Telephony Integration Follow him on Twitter:@jhoskin

In this episode of #BITTechTalk, Greg and Ayori welcome Joshua Hoskins to the podcast to talk about his journey to becoming a salesforce.com consulting MVP. Joshua shares valuable tips on driving your career, taking risks, starting his own company, relationship management, life hacking, work-life balance and how to get a job offer while attending dreamforce. We even manage to get into the nitty gritty details of data modeling on cloud platforms! Tune in because won’t want to miss this podcast!About our guest:Joshua has been using computers since the age of 4 and began writing code in the 5th grade. By age 22 he was Director of IT. While being extremely passionate about Customer Relationship Management & Application Development he lives to build reliable, robust and scalable solutions just about anything you can think of.Joshua’s specialties are Force.com Development, Salesforce.com Customization/Implementation, Data / Solution Architecture, Cloud & On-Premise Integrations, Configuration Management, Project Management, Ruby on Rails, ASP, VBScript, System Administration, Relational Databases, Networking, Computer Telephony Integration Follow him on Twitter:@jhoskin

Episode 33. Does code dream when it hibernates? A quick look on the big things of Hibernate ---- Databases have been around forever! (or at least from the 1970s) and there are many things that try to hook Object Objected Programming with Relational Databases (namely, O/R mappers). Hibernate is the most known O/R mapper and, while effective sometimes it can be confusing. In this podcast we delve on how to understand our misunderstood friend, and finally fix those pesky weird Hibernate exceptions once and for all! If you just joined us, remember to follow me on Twitter! (@fguime) and, well, it is Xmas! if you feel generous, and like what you hear, treat me a beer! Tweet, Tweet! (https://twitter.com/#!/fguime) Hibernate Mappings (Annotations) Hibernate Mappings (Annotation Cheat Sheet) Hibernate Mappings (on hbm) Database Normalization Database Normalization Vote for us in iTunes (http://itunes.apple.com/us/podcast/java-pub-house/id467641329) Questions, feedback or comments! comments@javapubhouse.com Subscribe to our podcast! (http://javapubhouse.libsyn.com/rss) ITunes link (http://itunes.apple.com/us/podcast/java-pub-house/id467641329) Java 7 Recipes book! (http://www.amazon.com/gp/product/1430240563/ref=as_li_ss_il?ie=UTF8&tag=meq-20&linkCode=as2&camp=1789&creative=390957&creativeASIN=1430240563) Hey! if you like what you hear, treat me a beer! (It's the Java pub house after all :) https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=Z8V2ZWV93UMW4

Aaron and Brian speak with Justin Sheehy (@justinsheehy) - CTO at Basho Technologies, about the evolution of applications, new database demands and why NoSQL is changing how we stored and access data.

In this show John Jardin comes back on to expand on his part 1 episode. He'll demonstrate more XPages and Relational Data integration and show you how to make things a little more flexible. The first part can be found here.

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Behind countless complex applications lurk trusty relational databases that are responsible for managing the data that fuel these applications. For example, relational databases are used to support electronic medical health record systems, timecard reporting systems, and transportation systems. Ideally, the relational database system has been sufficiently hardened to prevent exfiltration or modification of data. Unfortunately, adversaries often have insider access to the networks and machines on which the database is running and can easily circumvent such security measures. Therefore, in this research project, we create profiles of known, legitimate behavior so that we can flag any anomalous behavior as potentially illegitimate.In this presentation, because SQL injection remains the #1 attack vector, I will first illustrate how SQL injection attacks can exfiltrate data from a database system. I will then discuss various locations within the database engine that one might monitor activity, highlighting the benefits of placing a monitor between the query optimizer and query execution engine. Next, I will describe how we use cross-feature analysis to generate profiles of legitimate behavior and how these profile are used at run-time to identify anomalous activity. Then, I will present experimental results both in terms of performance overhead and precision/recall. I will conclude with a discussion of when our techniques are most applicable and how a clever adversary might nevertheless elude our monitor. About the speaker: Dr. Peter Mork is a Senior Technology Advisor and Principal Database Research at The MITRE Corporation. At MITRE his research revolves around data management topics including metadata management, data discovery, privacy and security. He also advises the Department of Health and Human Services on strategies for sharing data, particularly in the presence of privacy constraints. He received his PhD in 2005 from the University of Washington on the topic of Peer Architectures for Knowledge Sharing.

Behind countless complex applications lurk trusty relational databases that are responsible for managing the data that fuel these applications. For example, relational databases are used to support electronic medical health record systems, timecard reporting systems, and transportation systems. Ideally, the relational database system has been sufficiently hardened to prevent exfiltration or modification of data. Unfortunately, adversaries often have insider access to the networks and machines on which the database is running and can easily circumvent such security measures. Therefore, in this research project, we create profiles of known, legitimate behavior so that we can flag any anomalous behavior as potentially illegitimate. In this presentation, because SQL injection remains the #1 attack vector, I will first illustrate how SQL injection attacks can exfiltrate data from a database system. I will then discuss various locations within the database engine that one might monitor activity, highlighting the benefits of placing a monitor between the query optimizer and query execution engine. Next, I will describe how we use cross-feature analysis to generate profiles of legitimate behavior and how these profile are used at run-time to identify anomalous activity. Then, I will present experimental results both in terms of performance overhead and precision/recall. I will conclude with a discussion of when our techniques are most applicable and how a clever adversary might nevertheless elude our monitor.

In this espisode we take a closer look at relational database systems and the concepts behind them. We start by discussing the relational paradigm, its concepts and ramifications, and go on to architectural aspects.

In this espisode we take a closer look at relational database systems and the concepts behind them. We start by discussing the relational paradigm, its concepts and ramifications, and go on to architectural aspects.

In this espisode we take a closer look at relational database systems and the concepts behind them. We start by discussing the relational paradigm, its concepts and ramifications, and go on to architectural aspects.

Proving ownership rights on outsourced relational databases is a crucial issue in today internet-based application environment and in many content distribution applications. In this talk, we will present mechanisms for proof of ownership based on the secure embedding of a robust imperceptible watermark in relational data. We will discuss the available watermark embedding and decoding techniques. Furthermore, we will provide a comparison between these techniques based on several dimensions such as applicability, efficiency, and security. About the speaker: Mohamed Shehab received the BSc from United Arab Emirates University in 2000. Currently he is a PhD student in electrical and computer engineering at Purdue University. His main research interests lie in information security with emphasis on rights protection, data integrity and access control. Recently, he has been also working on various topics in the areas of distributed access control and distributed secure collaboration.

Proving ownership rights on outsourced relational databases is a crucial issue in today internet-based application environment and in many content distribution applications. In this talk, we will present mechanisms for proof of ownership based on the secure embedding of a robust imperceptible watermark in relational data. We will discuss the available watermark embedding and decoding techniques. Furthermore, we will provide a comparison between these techniques based on several dimensions such as applicability, efficiency, and security.