Podcasts about ksql

Highlights from this week's conversation include:Apruva's background in streaming technology (0:48)Developer experience and Kafka streams (2:47)Motivation to bootstrap a startup (4:09)Meeting the Confluent founders and early work at Confluent (6:59)Projects at Confluent and transition to engineering management (10:34)Overview of Responsive and event-driven applications (12:55)Defining event-driven applications (15:33)Importance of latency and state in event-driven applications (18:54)Low Latency and Stateful Processing (21:52)In-Memory Storage and Evolution of Kafka (25:02)Motivation for KSQL and Kafka Streams (29:46)Category Creation and Database-like Interface (34:33)Developer Experience with Kafka and Kafka Streams (38:50)Kafka Streams Functionality and Operational Challenges (41:44)Metrics and Tuning Configurations (43:33)Architecture and Decoupling in Kafka Streams (45:39)State Storage and Transition from RocksDB (47:48)Future of Event-Driven Architectures (56:30)Final thoughts and takeaways (57:36)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Unlock the secrets of engaging developer communities and the transformative world of real-time data analytics with our guest, Viktor Gamov of StarTree. From crafting code to leading developer relations, Viktor unravels his career evolution, highlighting how fostering connections and sharing knowledge with developers has reshaped the landscape of tech communication. His take on the democratization of technical know-how reveals the profound impact of making what was once consultancy-exclusive, accessible to all. Tune in for a masterclass on the importance of community in the tech industry and how it can break barriers for innovation.Are you ready to see data come to life? Viktor's thrilling exposition on Kafka, KSQL, and Apache Pino turns the arcane into the amazing, using a real-time Pac-Man game dashboard to illustrate the revolutionary shift from batch to stream processing. Witness the rebirth of open-source technologies and grasp the concept of 'data in motion' as we discuss the critical importance of streaming platforms in modern data architecture. Viktor's expertise in developer relations shines as he demonstrates the value of making complex tech relatable and relevant to business needs.The data landscape is ever-evolving, and with the rise of AI, the stakes have never been higher. In an era where milliseconds matter, Viktor peels back the layers of how Apache Pino is driving real-world solutions for industries galore. From restaurant load management to transaction tracking, discover how real-time analytics are informing strategic business decisions. As we journey with our guest back to his roots in data and game development, we're reminded of the cyclical nature of passion and profession—where one's beginnings often foretell the trajectory of their career. Don't miss out on this episode, where we connect the dots between nostalgia and the next wave of data innovation.What's New In Data is a data thought leadership series hosted by John Kutay who leads data and products at Striim. What's New In Data hosts industry practitioners to discuss latest trends, common patterns for real world data patterns, and analytics success stories.

Confluent's platform provides infrastructure for enterprises to connect, stream and process data across applications and systems in real time. In this episode of the Tech Disruptors podcast, Confluent's cofounder and CEO Jay Kreps joins Bloomberg Intelligence senior software analyst Sunil Rajgopal to discuss the origins of Apache Kafka and Confluent, the flow of enterprise data and future of software architecture. The two also talk about the opportunity arising from the shift toward real-time data streaming from batch processing, budding artificial intelligence workloads and the company's new products such as Confluent Cloud for Apache Flink, Kora Engine and KSQL database.

Follow: https://stree.ai/podcast | Sub: https://stree.ai/sub | New episodes every Monday! In this episode of the "Real-Time Analytics" podcast, Tim sits down with Hojjat Jafarpour, a leading figure in the streaming SQL domain. From the early days of KSQL to the cutting-edge work with DeltaStreams, they dive deep into the evolution and impact of real-time analytics, streaming SQL, and cloud-native data solutions. As a former colleague and friend, Hojjat shares his journey and insights on where the streaming world is heading. Join them for an insightful conversation that bridges the past and future of stream processing.

Beim Programmieren ist alles ein Algorithmus. Irgendwie zumindest. Doch wie misst man die Zeitkomplexität?Das ganze nennt sich Big-O-Notation, oder zu deutsch "Bachmann-Landau-Notation". Eigentlich ein recht trockenes Thema, doch auch irgendwie relevant in der heutigen Zeit von verteilten Systemen und großen Datenmengen. Doch was ist die Big-O-Notation? Was sagt sie aus? Wo kommt diese in der Praxis vor? Und inwieweit hat das ganze noch eine Relevanz in Zeiten von Cloud Computing und fast unbegrenzten Hardware-Ressourcen? Darum geht es in dieser Episode.Bonus: Wie Andy und Wolfgang in deutscher Grammatik belehrt werden, ob es OK ist in 1on1s zu fluchen und das Hash-Kollisionen mit der ganzen Sache zu tun haben.Feedback an stehtisch@engineeringkiosk.dev oder via Twitter an https://twitter.com/EngKioskLinksPocket Casts: https://pocketcasts.com/Learn Redis the hard way (in production): https://tech.trivago.com/post/learn-redis-the-hard-way/Redis KEYS Befehl: https://redis.io/commands/keys/Redis HSET Befehl: https://redis.io/commands/hset/kSQL: https://www.confluent.io/de-de/blog/ksql-streaming-sql-for-apache-kafka/Engineering Kiosk auf LinkedIn: https://www.linkedin.com/company/engineering-kiosk/Sprungmarken(00:00:00) Intro(00:00:49) Intro: Feedback zur Episode #26 - Deutschland spricht schlecht Englisch(00:03:55) Intro: Feedback zur Grammatik - Der, die oder das Kommentar(00:06:05) Wer hört denn so alles das Engineering Kiosk?(00:07:09) Podcast-Player: Pocket Casts(00:08:32) Was sind die Sachen, die oft sinnvoll sind, du aber doch vergessen hast?(00:10:34) Das heutige Thema: Big-O Notation / Bachmann-Landau Notation (Zeitkomplexität von Algorithmen)(00:13:33) Warum ist die Big-O Notation / Bachmann-Landau Notation relevant(00:15:24) Wo war es das letzte mal wo dir die Big-O Notation vorgekommen ist(00:16:05) Was ist die Big-O Notation / Bachmann-Landau Notation?(00:20:59) Geben wir den best, average oder worst-case der Big-O Notation / Bachmann-Landau Notation an?(00:25:15) Konstanten können zur Vereinfachung einfach weggelassen werden(00:27:00) Big-O Notation bei redis: Die Story wie Andy damit in Berührung kam(00:31:16) Zeitkomplexität vs. Space-Complexity / Raumkomplexität: Zeit vs. Memory(00:32:58) Gibt es was besseres als O(1)?(00:34:26) Ist das setzen eines Keys in einer Hashmap immer O(1)?(00:40:03) Inwieweit kann man die Big-O Notation / Bachmann-Landau Notation auf Datenbanken mappen?(00:42:07) Wie relevant ist die Optimierung der Big-O Notation in Zeiten von Cloud und schnellen Servern eigentlich noch?(00:45:52) Batch-Processing vs. Streaming und die Optimierung von Algorithmen(00:48:49) Optimierungen von Algorithmen in JavaScript / auf Client-Seite(00:52:06) Optimierungen können auch schlecht sein bzw. schlecht aussehen(00:53:09) Outro mit FlachwitzenHostsWolfgang Gassler (https://twitter.com/schafele)Andy Grunwald (https://twitter.com/andygrunwald)Engineering Kiosk Podcast: Anfragen an stehtisch@engineeringkiosk.dev oder via Twitter an https://twitter.com/EngKiosk

Today on the show we're talking about streaming data and streaming applications with Apache Kafka. We're joined by Kris Jenkins, Developer Advocate from Confluent, and Rob Walters, Product Manager at MongoDB, who will discuss how you can leverage this technology to your benefit and use it in your applications.Kafka is traditionally used for building real time streaming data pipelines and real time streaming applications. It began its life in 2010 at LinkedIn and made its way to the public open-source space through a relationship with Apache, the Apache Foundation, in 2011. Since then, the use of Kafka has grown massively and it's estimated that approximately 30% of all Fortune 500 companies are already using Kafka in one way or another. A great example for why you might want to use Kafka would be perhaps capturing all of the user activity that happens on your website. As users visit your website, they're interacting with links on the page and scrolling up and down. This is potentially large volumes of data. You may want to store this to understand how users are interacting with your website in real time. Kafka will aid in this process by ingesting and storing all of this activity data while serving up reads for applications on the other side. Conversation highlights include:[03:38] What is Kafka?[05:29] At the heart of every database[08:03] The difference between Kafka and a database[09:03] What Kafka's architecture looks like[12:03] Kafka as a data backbone of system architecture[14:06] MongoDB and Kafka working together[15:40] What are "Topics" in Kafka?[17:53] Chain stream events[19:58] Kafka's history[22:07] MongoDB Connector, and Kafka via Confluent Cloud[25:53] Popular use cases using Kafka and MongoDB[27:48] Kafka and stream processing with games and event data[29:13] KSQL and processing against the stream of data[30:59] Developer.Confluence.io, a place to learn everything about Kafka

Mark Rittman is joined in this episode by Michale Drogalis, Product Manager at Confluent to talk about the history of Apache Kafka and real-time stream processing, the origins of kSQL and kSQLdb and the rationale and use-cases addressed by these products.ksqldDB product homepage Introducing ksqlDB Blog PostKafka Streams vs. ksqlDB Blog Post

Mark Rittman is joined in this episode by Michale Drogalis, Product Manager at Confluent to talk about the history of Apache Kafka and real-time stream processing, the origins of kSQL and kSQLdb and the rationale and use-cases addressed by these products.ksqldDB product homepage Introducing ksqlDB Blog PostKafka Streams vs. ksqlDB Blog Post

What is the internet of things (IoT), and how does it relate to event streaming and Apache Kafka®? The deployment of Kafka outside the datacenter creates many new possibilities for processing data in motion and building new business cases.In this episode, Kai Waehner, field CTO and global technology advisor at Confluent, discusses the intersection of edge data infrastructure, IoT, and cloud services for Kafka. He also details how businesses get into the sticky situation of not accounting for solutions when data is running dangerously close to the edge. Air-gapped environments and strong security requirements are the norm in many edge deployments.Defining the edge for your industry depends on what sector you’re in plus the amount of data and interaction involved with your customers. The edge could lie on various points of the spectrum and carry various meanings to various people. Before you can deploy Kafka to the edge, you must first define where that edge is as it relates to your connectivity needs. Edge resiliency enables your enterprise to not only control your datacenter with ease but also preserve the data without privacy risks or data leaks. If a business does not have the personnel to handle these big IT jobs on their own or an organization simply does not have an IT department at all, this is where Kafka solutions can come in to fill the gap. This podcast explores use cases and architectures at the edge (i.e., outside the datacenter) across industries, including manufacturing, energy, retail, restaurants, and banks. The trade-offs of edge deployments are compared to a hybrid integration with Confluent Cloud. EPISODE LINKSProcessing IoT Data End to End with MQTT & KafkaEnd-to-End Integration: IoT Edge to ConfluentInfrastructure Checklist for Kafka at the EdgeUse Cases & Architectures for Kafka at the EdgeArchitecture Patterns for Distributed, Hybrid, Edge & Global Kafka DeploymentsBuilding a Smart Factory with Kafka & 5G Campus NetworksKafka Is the New Black at the Edge in Industrial IoT, Logistics & Retailing Kafka, KSQL & Apache PLC4X for IIoT Data Integration & Processing Streaming Machine Learning at Scale from 100K IoT Devices with HiveMQ, Kafka & TensorFlow Watch this podcast on YouTubeJoin the Confluent CommunityUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

Jay Kreps, CEO and Co-founder of Confluent discusses ksqlDB which is a database built specifically for stream processing applications to query streaming events in Kafka with SQL like interface.

Gwen Shapira (Engineering Leader, Confluent), Ben Stopford (Senior Director, OCTO, Confluent), and Michael Noll (Principal Technologist, Confluent) meet up to talk all about their last five years at Confluent and the changes they’ve seen in event streaming. They discuss what they were doing with Apache Kafka® before they arrived at Confluent, challenges in event streaming challenges that have arisen, and their favorite use cases. They then talk through what they think the Kafka community is undervaluing and where they think event streaming will be in the next five years. EPISODE LINKSTim’s Budapest Drone Footage Rolling Kafka Upgrades and Confluent Cloud ft. Gwen ShapiraDistributed Systems Engineering with Apache Kafka ft. Gwen ShapiraImproving Fairness Through Connection Throttling in the Cloud with KIP-402 ft. Gwen ShapiraAsk ConfluentApache Kafka Fundamentals: The Concept of Streams and Tables ft. Michael NollBen Stopford on Microservices and Event StreamingThe Portable Wonder Synthesizer Children's Hospital of Atlanta: Helping Healthcare with Apache Kafka and KSQL ft. Ramesh SringeriJoin the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

If you’ve ever wondered what Apache Kafka® is, what it’s used for, or wanted to learn about Kafka architecture and all its components, buckle up! In today’s episode, Michael Noll (Principal Technologist, Confluent) and Tim Berglund (Senior Director of Developer Advocacy, Confluent) discuss a series of fundamental questions: What is Kafka? What is an event? How do we organize and store events? And what is Kafka Streams? Over the course of this episode, Michael covers an in-depth look into Kafka technology and core concepts: the process of reading from a topic, differences between tables and streams, mutability, and what ksqlDB is and what its event streaming database features accomplish. If you've ever wanted to get a better grasp on how Kafka works, this episode is for you!EPISODE LINKSStreams and Tables in Apache Kafka: A PrimerStreams and Tables in Apache Kafka: Topics, Partitions, and Storage FundamentalsStreams and Tables in Apache Kafka: Processing Fundamentals with Kafka Streams and ksqlDBStreams and Tables in Apache Kafka: Elasticity, Fault Tolerance, and other Advanced ConceptsBrowse the Confluent HubJoin the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent Developer

There are two primary industries within the Internet of Things (IoT): industrial IoT (IIoT) and consumer IoT (CIoT), both of which can benefit from the Apache Kafka® ecosystem, including Kafka Streams and Kafka Connect. Kai Waehner, who works in the advanced tech group at Confluent with customers, defining their needs, use cases, and architecture, shares example use cases where he’s seen IoT integration in action. He specifically focuses on Walmart and its real-time customer integration using the Walmart app. Kafka Streams helps fine-tune the Walmart app, optimizing the user experience, offering a seamless omni-channel experience, and contributing to business success. Other topics discussed in today’s episode include integration from various legacy and modern IoT data sources, latency sensitivity, machine learning for quality control and predictive maintenance, and when event streaming can be more useful than traditional databases or data lakes.EPISODE LINKSApache Kafka 2.5 – Overview of Latest Features, Updates, and KIPsMachine Learning with Kafka Streams, Kafka Connect, and ksqlDB ft. Kai WaehnerBlog posts by Kai WaehnerProcessing IoT Data from End to End with MQTT and Apache Kafka®End-to-End Integration: IoT Edge to Confluent CloudApache Kafka is the New Black at the Edge in Industrial IoT, Logistics, and RetailingApache Kafka, KSQL, and Apache PLC4X for IIoT Data Integration and ProcessingStreaming Machine Learning at Scale from 100,000 IoT Devices with HiveMQ, Apache Kafka, and TensorFlowEvent-Model Serving: Stream Processing vs. RPC with Kafka and TensorFlowJoin the Confluent Community SlackLearn about Kafka at Confluent Developer

A quick summary of new features, updates, and improvements in Confluent Platform 5.4, including Role-Based Access Control (RBAC), Structured Audit Logs, Multi-Region Clusters, Confluent Control Center enhancements, Schema Validation, and the preview for Tiered Storage.This release also includes pull queries and embedded connectors in preview as part of KSQL.EPISODE LINKSConfluent Platform 5.4 Release Notes Introducing Confluent Platform 5.4Download Confluent Platform 5.4Watch the video version of this podcastJoin us in Confluent Community SlackGet 30% off Kafka Summit London registration with the code KSL20Audio

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.

Migrating from traditional ETL tools to an event streaming platform is a process that Stewart Bryson (CEO and founder, Red Pill Analytics), is no stranger to. In this episode, he dispels misconceptions around what “streaming ETL” means, and explains why event streaming and event-driven architectures compel us to rethink old approaches:Not all data is corporate data anymoreNot all data is relational data anymoreThe cost of storing data is now negligibleSupporting modern, distributed event streaming platforms, and the shift of focus from on-premises to the cloud introduces new use cases that focus primarily on building new systems and rebuilding existing ones. From Kafka Connect and stack applications to the importance of tables, events, and logs, Stewart also discusses Gradle and how it’s being used at Red Pill Analytics. EPISODE LINKSDeploying Kafka Streams and KSQL with Gradle – Part 1: Overview and MotivationDeploying Kafka Streams and KSQL with Gradle – Part 2: Managing KSQL ImplementationsDeploying Kafka Streams and KSQL with Gradle – Part 3: KSQL User-Defined Functions and Kafka Streams Join the Confluent Community SlackFully managed Apache Kafka as a service! Try free.

In today’s episode of Streaming Audio, Tim Berglund sits down with Senior Applications Developer of Mobile Solutions Ramesh Sringeri to discuss Apache Kafka®—specifically two Kafka use cases that Children’s Healthcare of Atlanta is working on.First, they discuss achieving near-real-time streams of data to support meaningful intracranial pressure prediction and managing intracranial pressure (ICP) in a timely manner to help the care team achieve better outcomes with traumatic brain injuries.Children’s Healthcare of Atlanta is in the process of building machine learning models for predicting ICP values 30 and 60 minutes in the future. This will help the care team better prepare for handling potential adverse conditions, where elevated ICP values could lead to undesirable outcomes. The Children’s team is using Kafka, KSQL, and Kafka Streams programs to build a pipeline in which they can test their machine learning models.Ramesh also shares about the work they’re doing to mitigate alarm fatigue for care providers. According to him, the current generation of monitoring devices are not equipped to set up multiple alarm conditions, and sometimes a combination of measures need to cross thresholds to be of concern. Children’s is able to leverage stream processing and KSQL to set up multiple conditions, reducing the number of meaningless alarms conditions that might condition care providers to ignore them.One of the best parts of it all—with Kafka and KSQL, the Children’s team has been able to quickly build data processing pipelines and address business use cases without having to write a lot of code.EPISODE LINKSJoin the Confluent Community SlackFor more, you can check out ksqlDB, the successor to KSQL.

San Carlos Airport has very specific departure procedures to limit the noise of airplanes above residential areas. Right now, KSQL has the Bay Meadows, Woodside, and Belmont Slough departures, but at the end of the month, new noise abatement procedures will be released. Every pilot will need to immediately adopt these new guidelines. In this seminar, we have KSQL Airport Communications Specialist Davi Howard explaining the new departure names and procedures, something every pilot flying to and from KSQL needs to know. Come join us before San Carlos Flight Center’s July BBQ for this seminar.

Tim Berglund and Michael Drogalis (Product Lead for Kafka Streams and KSQL, Confluent) talk about all things stream processing: why it’s complex, how it's evolved, and what’s on the horizon to make it simpler.

Gwen and Kai chat about machine learning architectures, and whether software engineers and data scientists can learn to get along.EPISODE LINKSBlogs on deploying machine learning workloads: Machine Learning with Python, Jupyter, KSQL and TensorFlowHow to Build and Deploy Scalable Machine Learning in Production with Apache KafkaUsing Apache Kafka to Drive Cutting-Edge Machine LearningKIP-392: Allow consumers to fetch from closest replica

Microservices are pretty ubiquitous these days. Really “SOA done right,” they reimagine the services pattern in the context of the world we live in today, nearly two decades since the first big service-oriented systems hit production. But what have we learned in this time? There are plenty of war stories. System designers have explored different architectural patterns—REST, events and databases of all types. In this podcast, Tim Berglund and Ben Stopford explore the event-driven paradigm and how it relates to the microservice architectures we build today. Ben dives deep into coupling, evolution and challenges of our increasingly data-oriented culture. He also talks about the future, where data are events and events are data, and touches on real-time architectures that retain the decoupling properties needed to be pluggable, and to evolve. Powerful stuff.EPISODE LINKSDesigning Event-Driven Systems Building a Microservices Ecosystem with Kafka Streams and KSQL

We all know that feeling of waiting when your ride is running late. Leslie Kurt shares about how you can use KSQL to calculate the difference between the expected arrival time and real-time updates of a bus as it executes its route. Listen as Leslie walks you through fundamental concepts like KTables, Kafka Streams, persistent queries and Confluent MQTT Proxy, as well as other use cases that involve a similar mechanism of capturing Unix timestamps and performing a stream processing operation on these timestamps.EPISODE LINKSAbout KSQLStream Processing CookbookKSQL Recipe: Calculating Bus Delay TimeFor more, you can check out ksqlDB, the successor to KSQL.

Tim Berglund chats with System Engineer Pascal Vantrepote about a KSQL recipe he created based on a real-life customer use case in the financial services industry. They also discuss the advantages of KSQL, such as its expressiveness and ease of deployment in places where you’re not already writing a Java application.EPISODE LINKSAbout KSQL Stream Processing CookbookKSQL Recipe: Data Routing Joined with a KTableFor more, you can check out ksqlDB, the successor to KSQL.

Viktor Gamov is on the podcast today to discuss Confluent and Kafka with Mark and special first-time guest host, Michelle. Viktor spends time with Mark and Melanie explaining how Kafka allows you to stream and process data in real-time, and how Kafka helps Confluent with its advanced streaming capabilities. Confluent Cloud helps connect Confluent and cloud platforms such as Google Cloud so customers don’t have to manage anything - Confluent takes care of it for you! To wrap up the show, Michelle answers our question of the week about Next 2019. Viktor Gamov Viktor Gamov is a Developer Advocate at Confluent, the company that makes a streaming platform based on Apache Kafka. Working in the field, Viktor developed comprehensive expertise in building enterprise application architectures using open source technologies. He enjoys helping different organizations design and develop low latency, scalable, and highly available distributed systems. Back in his consultancy days, he co-authored O’Reilly’s «Enterprise Web Development». He is a professional conference speaker on distributed systems, Java, and JavaScript topics, and is a regular at events, including JavaOne, Devoxx, OSCON, QCon, and others. He blogs and produces the podcasts Razbor Poletov (in Russian) and co-hosts DevRelRad.io. Follow Viktor on Twitter, where he posts about gym life, food, open source, and, of course, Kafka and Confluent! Cool things of the week Kubeflow published a leadership guide to inclusivity site Picture what the cloud can do: How the New York Times is using Google Cloud to find untold stories in millions of archived photos blog Click-to-deploy on Kubeflow site Containerd available for beta testing in Google Kubernetes Engine blog Introducing AI Hub and Kubeflow Pipelines: Making AI simpler, faster, and more useful for businesses blog Announcing Cloud Scheduler: a modern, managed cron service for automated batch jobs blog Interview Kafka site Kafka Connect site Kafka Streams site KSQL site Confluent site Confluent Hub site Confluent Schema Registry site Confluent Cloud on Google Cloud Marketplace site Confluent Enterprise site Confluent Cloud site Confluent on Github site Confluent Blog blog How to choose the number of topics/partitions in a Kafka cluster? blog Publishing with Apache Kafka at The New York Times blog Google Cloud Platform and Confluent partner to deliver a managed Apache Kafka service blog Viktor’s Presentations site Confluent Community site Question of the week If I wanted to submit a CFP for Next 2019, how would I do it? Where can you find us next? Mark and Michelle will be at KubeCon in December. Michelle will be at Scale by the Bay on Friday. She’ll also be at YOW! Sydney, Brisbane, & Melbourne in Nov & December.

Tim Berglund interviews Bill Bejeck about the Kafka Streams API and his new book, Kafka Streams in Action.

KSQL 5.0 now supports stream-stream, stream-table and table-table joins. Tim Berglund interviews Hojjat Jafarpour about all three join types, how they work, what their limitations are and the new kinds of operations they unlock.For more, you can check out ksqlDB, the successor to KSQL.

Gwen is joined in studio by co-host Tim Berglund and special guest, Viktor Gamov, a new member of Confluent’s Developer Experience Team specializing in Kafka, KSQL and Kubernetes. In this episode, we’ll find out: Does Viktor know what he’s talking about?

A discussion about how people actually use KSQL with Nick Dearden, stream processing expert at Confluent. Try KSQL!For more, you can check out ksqlDB, the successor to KSQL.

Interesting data isn't a polite little list of scalar types. Sometimes you have more complex structures and things like nesting. We'll see how KSQL supports that today as Tim Berglund discusses nested data in KSQL with Hojjat Jafarpour, a software engineer on the KSQL team at Confluent. EPISODE LINKSKSQL demos and infoKSQL GitHub KSQL Slack (#ksql channel) For more, you can check out ksqlDB, the successor to KSQL.

KSQL has a solid library of built-in functions, but no library is ever good enough. What if you want to write your own? We’ll learn how today with Hojjat Jafarpour, a software engineer on the KSQL team at Confluent.For more, you can check out ksqlDB, the successor to KSQL.

Want to see a feature implemented in KSQL or other Kafka-related project? Gwen answers your questions from YouTube and walks through how to use GitHub issues to request features. This is the episode #4 of "Ask Confluent," a segment in which Gwen Shapira and guests respond to a handful of questions and comments from Twitter, YouTube and elsewhere.

Ever wonder what actually goes on when you run a KSQL query? Today, we take a deep dive into KSQL with Hojjat Jafarpour, a software engineer on the KSQL team at Confluent.For more, you can check out ksqlDB, the successor to KSQL.

Tim Berglund and Gwen Shapira have a discussion with Koelli Mungee (Customer Operations Lead, Confluent) and cover the latest Apache Kafka upgrades, cloud APIs, and data durability. This is episode #3 of "Ask Confluent," a segment in which Gwen Shapira and guests respond to a handful of questions and comments from Twitter, YouTube, and elsewhere.EPISODE LINKSFully managed Apache Kafka as a service! Try free.

Gwen Shapira answers your questions and interviews Sam Hecht (Head of Support, Confluent). This is the second episode of "Ask Confluent," a segment in which Gwen Shapira and guests respond to a handful of questions and comments from Twitter, YouTube and elsewhere.

Tim Berglund and Gwen Shapira discuss Kubernetes, Confluent Operator, Kafka, KSQL, and more. This is the first episode of "Ask Confluent," a segment in which Gwen Shapira and guests respond to a handful of questions and comments from Twitter, YouTube and elsewhere.

In this episode of the ARCHITECHT Show, Confluent co-founder and CTO Neha Narkhede talks about the Apache Kafka's evolution from a scalable message bus into a critical component of modern application architectures. This includes everything from feeding data into machine learning pipelines to analyzing streaming data using the new KSQL feature. Narkhede also discusses the importance of cloud-native architectures and how platforms from AWS to Kubernetes are influencing how Confluent thinks about its business.

Exactly-once Semantics are Possible: Here’s How Kafka Does ithttps://www.confluent.io/blog/exactly-once-semantics-are-possible-heres-how-apache-kafka-does-it/?utm_content=buffer9b1b6&utm_medium=social&utm_source=twitter.com&utm_campaign=bufferhttps://blog.ippon.fr/2017/07/11/kafka-0-11-0-%E2%99%A5/Confluent KSQLhttps://www.confluent.io/blog/ksql-open-source-streaming-sql-for-apache-kafka/https://www.youtube.com/watch?v=A45uRzJiv7I&feature=youtu.beKafka + Prestodb.iohttps://prestodb.io/docs/current/connector/kafka.htmlStreaming SQL in Apache Flink, KSQL, and Stream Processing for Everyonehttps://data-artisans.com/blog/flink-streaming-sql-ksql-stream-processingKafka Wakes Up And Is Metamorphosed Into A Databasehttps://www.nextplatform.com/2017/08/30/kafka-wakes-metamorphosed-database/amp/(Editor’s Note: It would have been far funnier, of course, if Kafka woke up one morning and had been turned into CockroachDB.)Open sourcing Kafka cruise controlhttps://engineering.linkedin.com/blog/2017/08/open-sourcing-kafka-cruise-controlhttps://github.com/linkedin/cruise-controlYahoo’s New Pulsar: A Kafka Competitor?https://www.datanami.com/2016/09/07/yahoos-new-pulsar-kafka-competitor/Apache Beam 2.1https://beam.apache.org/get-started/downloads/Apache Beam splittable DoFnhttps://beam.apache.org/blog/2017/08/16/splittable-do-fn.htmlInstaclustr Dynamic Resizing for Apache Cassandrahttps://www.instaclustr.com/instaclustr-dynamic-resizing-for-apache-cassandra/?utm_content=buffer624e7&utm_medium=social&utm_source=twitter.com&utm_campaign=bufferRiak devs giddy over gambling biz's vow to set code freehttps://www.theregister.co.uk/2017/08/25/bet365_to_buy_basho_release_code/?mt=1503782778086Spark Release 2.2.0http://spark.apache.org/releases/spark-release-2-2-0.html[mooc] Specialisation Data-Engineering Google Cloud sur Courserahttps://fr.coursera.org/specializations/gcp-data-machine-learning[podcast] Y a-t-il un cerveau dans la machine ? une interview de Yann Le Cun, directeur du FAIRhttps://www.franceculture.fr/emissions/la-methode-scientifique/y-t-il-un-cerveau-dans-la-machine[podcast] DREMEL, DRUID AND DATA MODELING ON GOOGLE BIGQUERY' https://www.drilltodetail.com/podcast/2017/6/19/drill-to-detail-ep31-dremel-druid-and-data-modeling-on-google-bigquery-with-special-guest-dan-mcclary[privacy] comment les apps Figaro, L’Équipe ou Closer participent au pistage de 10 millions de Françaishttp://www.numerama.com/politique/282934-enquete-comment-les-apps-figaro-lequipe-ou-closer-participent-au-pistage-de-10-millions-de-francais.htmlComment l’intelligence artificielle bouleverse l’industrie des médiashttp://www.latribune.fr/opinions/tribunes/comment-l-intelligence-artificielle-bouleverse-l-industrie-des-medias-746917.htmlCédric Villani est chargé d'une mission d'information parlementaire sur l'IA.http://www.numerama.com/politique/286341-le-gouvernement-fait-appel-a-cedric-villani-pour-une-mission-sur-lia.html-------------------------------------------------------------http://www.bigdatahebdo.comhttps://twitter.com/bigdatahebdoVincent : https://twitter.com/vhe74Alexander : https://twitter.com/alexanderdeja Cette publication est sponsorisée par Affini-Tech ( http://affini-tech.com https://twitter.com/affinitech )On recrute ! venez cruncher de la data avec nous ! écrivez nous à recrutement@affini-tech.com

Exactly-once Semantics are Possible: Here’s How Kafka Does ithttps://www.confluent.io/blog/exactly-once-semantics-are-possible-heres-how-apache-kafka-does-it/?utm_content=buffer9b1b6&utm_medium=social&utm_source=twitter.com&utm_campaign=bufferhttps://blog.ippon.fr/2017/07/11/kafka-0-11-0-%E2%99%A5/Confluent KSQLhttps://www.confluent.io/blog/ksql-open-source-streaming-sql-for-apache-kafka/https://www.youtube.com/watch?v=A45uRzJiv7I&feature=youtu.beKafka + Prestodb.iohttps://prestodb.io/docs/current/connector/kafka.htmlStreaming SQL in Apache Flink, KSQL, and Stream Processing for Everyonehttps://data-artisans.com/blog/flink-streaming-sql-ksql-stream-processingKafka Wakes Up And Is Metamorphosed Into A Databasehttps://www.nextplatform.com/2017/08/30/kafka-wakes-metamorphosed-database/amp/(Editor’s Note: It would have been far funnier, of course, if Kafka woke up one morning and had been turned into CockroachDB.)Open sourcing Kafka cruise controlhttps://engineering.linkedin.com/blog/2017/08/open-sourcing-kafka-cruise-controlhttps://github.com/linkedin/cruise-controlYahoo’s New Pulsar: A Kafka Competitor?https://www.datanami.com/2016/09/07/yahoos-new-pulsar-kafka-competitor/Apache Beam 2.1https://beam.apache.org/get-started/downloads/Apache Beam splittable DoFnhttps://beam.apache.org/blog/2017/08/16/splittable-do-fn.htmlInstaclustr Dynamic Resizing for Apache Cassandrahttps://www.instaclustr.com/instaclustr-dynamic-resizing-for-apache-cassandra/?utm_content=buffer624e7&utm_medium=social&utm_source=twitter.com&utm_campaign=bufferRiak devs giddy over gambling biz's vow to set code freehttps://www.theregister.co.uk/2017/08/25/bet365_to_buy_basho_release_code/?mt=1503782778086Spark Release 2.2.0http://spark.apache.org/releases/spark-release-2-2-0.html[mooc] Specialisation Data-Engineering Google Cloud sur Courserahttps://fr.coursera.org/specializations/gcp-data-machine-learning[podcast] Y a-t-il un cerveau dans la machine ? une interview de Yann Le Cun, directeur du FAIRhttps://www.franceculture.fr/emissions/la-methode-scientifique/y-t-il-un-cerveau-dans-la-machine[podcast] DREMEL, DRUID AND DATA MODELING ON GOOGLE BIGQUERY' https://www.drilltodetail.com/podcast/2017/6/19/drill-to-detail-ep31-dremel-druid-and-data-modeling-on-google-bigquery-with-special-guest-dan-mcclary[privacy] comment les apps Figaro, L’Équipe ou Closer participent au pistage de 10 millions de Françaishttp://www.numerama.com/politique/282934-enquete-comment-les-apps-figaro-lequipe-ou-closer-participent-au-pistage-de-10-millions-de-francais.htmlComment l’intelligence artificielle bouleverse l’industrie des médiashttp://www.latribune.fr/opinions/tribunes/comment-l-intelligence-artificielle-bouleverse-l-industrie-des-medias-746917.htmlCédric Villani est chargé d'une mission d'information parlementaire sur l'IA.http://www.numerama.com/politique/286341-le-gouvernement-fait-appel-a-cedric-villani-pour-une-mission-sur-lia.html-------------------------------------------------------------http://www.bigdatahebdo.comhttps://twitter.com/bigdatahebdoVincent : https://twitter.com/vhe74Alexander : https://twitter.com/alexanderdeja Cette publication est sponsorisée par Affini-Tech ( http://affini-tech.com https://twitter.com/affinitech )On recrute ! venez cruncher de la data avec nous ! écrivez nous à recrutement@affini-tech.com

This episode I take you on a classic Nicnac trip in the Bonza A36 from my home airport KCIC (Chico, CA) to KSQL (San Carlos, CA). This is flight simulation at its best pure and simple. I live for simulated flights in beautiful planes such as these. So sit back relax and enjoy the biscuits as we ride to the bay area from the north Sacramento Valley.   Due to limited production time the audio may be poor in some spots. If you complain enough I may edit an rerelease    Go forth and email nicnacjak at gmail dot com Go forth and follow me on Twitter or Facebook using the links on the right Donate. I am poor and I need money…you know to go to England. Check out my stream Nicnac Radio in: Winamp Windows Media Player Itunes Realplayer   --- Send in a voice message: https://anchor.fm/nicnacjak/message