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Tim Berglund picks the brain of a distributed systems engineer, Guozhang Wang, tech lead in the Streaming department of Confluent. Guozhang explains what compelled him to join the Stream Processing team at Confluent coming from the Apache Kafka®  core infrastructure. He reveals what makes the best distributed systems infrastructure engineers tick and how to prepare to take on this kind of role—solving failure scenarios, a satisfying challenge. One challenge in distributed systems is achieving agreements from multiple nodes that are connected in a Kafkacluster, but the connection in practice is asynchronous.Guozhang also shares the newest updates in the Kafka community, including the coming ZooKeeper-free architecture where metadata will be maintained by Kafka logs.Prior to joining Confluent, Guozhang worked for LinkedIn, where he used Kafka for a few years before he started asking himself, “How fast can I get value from the data that I’ve collected?” This question eventually led him to begin building Kafka Streams and ksqlDB. Ever since, he’s been working to advance stream processing, and in this episode, provides an exciting preview of what’s to come. EPISODE LINKSJoin the Confluent teamDiving into Exactly-Once Semantics with Guozhang WangIn Search of an Understandable Consensus AlgorithmThe Curious Incident of the State Store in Recovery in ksqlDBFrom Eager to Smarter in Apache Kafka Consumer RebalancesKIP-595: A Raft Protocol for the Metadata QuorumJoin the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

Confluent Cloud and Confluent Platform run efficiently largely because of the dedication of the Developer Productivity (DevProd) team, formerly known as the Tools team. Mohinish Shaikh (Software Engineer, Confluent) talks to Tim Berglund about how his team builds the software tooling and automation for the entire event streaming platform and ensures seamless delivery of several engineering processes across engineering and the rest of the org. With the right tools and the right data, developer productivity can understand the overall effectiveness of a development team and their ability to produce results.The DevProd team helps engineering teams at Confluent ship code from commit to end customers actively using Apache Kafka®. This team proficiently understands a wide scope of polyglot applications and also the complexities of using a diverse technology stack on a regular basis to help solve business-critical problems for the engineering org. The team actively measures how each system interacts with one another and what programs are needed to properly run the code in various environments to help with the release of reliable artifacts for Confluent Cloud and Confluent Platform. An in-depth understanding of the entire framework and development workflow is essential for organizations to deliver software reliably, on time, and within their cost budget.The DevProd team provides that second line of defense and reliability before the code is released to end customers. As the need for compliance increases and the event streaming platform continues to evolve, the DevProd team is in place to make sure that all of the final touches are completed. EPISODE LINKSLeveraging Microservices and Apache Kafka to Scale Developer ProductivityJoin the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

What’s it like being a Microsoft Azure Cloud advocate working with Apache Kafka® and change data capture (CDC) solutions? Abhishek Gupta would know! At Microsoft, Abhishek focuses his time on Kafka, Databases, Kubernetes, and open source projects. His experience in a wide variety of roles ranging from engineering, consulting, and product management for developer-focused products has positioned him well for developer advocacy, where he is now.Switching gears, Abhishek proceeds to break down the concept of CDC starting off with some of the core concepts such as "commit logs." Abhishek then explains how CDC can turn data around when you compare it to the traditional way of querying the database to access data—you don't call the database; it calls you. He then goes on to discuss Debezium, which is an open source change data capture solution for Kafka. He also covers Azure connectors, Azure Data Explorer, and use cases powered by the Azure Data Explorer Sink Connector for Kafka.EPISODE LINKSStreaming Data from Confluent Cloud into Azure Data ExplorerIntegrate Apache Kafka with Azure Data ExplorerChange Data Capture with Debezium ft. Gunnar MorlingTales from the Frontline of Apache Kafka DevOps ft. Jason BellMySQL CDC Source (Debezium) Connector for Confluent CloudMySQL, Cassandra, BigQuery, and Streaming Analytics with Joy GaoJoin the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

Coming out of a whirlwind year for the event streaming world, Tim Berglund sits down with Gwen Shapira (Engineering Leader, Confluent), Ben Stopford (Senior Director, Office of the CTO, Confluent), and Michael Noll (Principal Technologist, Office of the CTO, Confluent) to take a guess at what 2021 will bring. The experts share what they believe will happen for analytics, frameworks, multi-cloud services, stream processing, and other topics important to the event streaming space. These Apache Kafka® related predictions include the future of the Kafka cluster partitions and removing restrictions that users have found in the past, such as too many variations and excessive concurrency as it relates to your number of partitions.Ben also thinks that ZooKeeper will continue to maintain open source servers for highly reliable application distribution. Kafka clusters will still be able to keep the most important data while growing in size at record speed with ZooKeeper, although it will no longer be required with KIP-500 removing ZooKeeper dependency. This upgrade allows Kafka and ZooKeeper to run independently in deployment while Kafka’s cluster capability will increase.Michael expects a continued need for COVID-19 tracking as well as enhanced event streaming capabilities. Ben believes that scalable Tiered Storage for Kafka will increase productivity and benefit workloads. Gwen predicts that databases will become more conventional by the end of next year, leading to new data architectural design with the support of Kafka.EPISODE LINKSKIP-500: Apache Kafka Without ZooKeeper ft. Colin McCabe and Jason GustafsonHow to set up podcasts on AlexaBetter to Be Wrong Than Vague: Apache Kafka and Data Architecture Predictions for 2021Join the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

It’s one thing to know how to use Apache Kafka® and another to prove it to the world that you know. Niamh O’Byrne (Certification Manager, Confluent) and Barry Ballard (Senior Technical Trainer, Confluent) discuss Confluent’s Certification program, including sample test questions, bootcamp, exam details, Kafka training, and getting the necessary practical hands-on experience.It’s no secret that the entire world of work has changed, and now we expect to communicate across a vast number of digital platforms. In this new age, Barry predicts three primary skills that will become more important than ever to employers as they seek to hire a candidate:Emotional intelligenceBuilding your personal brand Digital security knowledgeWith emotional intelligence, we're really talking about effective communication and soft skills. This means understanding how to achieve consensus on utilizing digital technology, specifically Apache Kafka, which we test for in the Certification exam. This will help you stand out all around—on paper, in interviews, and in knowledge too. Especially as more and more businesses rely on Kafka, and as cybercriminals take their savviness to a new level, strong security expertise will truly set you apart.EPISODE LINKSConfluent Certification Get in touch about the Certification at certification@confluent.ioUsing Event Modeling to Architect Event-Driven Information Systems ft. Bobby CalderwoodLearn Apache Kafka to build and scale modern applicationsProject MetamorphosisJoin the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse the code DEV21CERT for 20% off certificationUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

How do you use Apache Kafka®, Confluent Platform, and Confluent Cloud for DevOps? Integration Architects Rick Spurgeon and Allison Walther share how,  including a custom tool they’ve developed for this very purpose. First, Rick and Allison share their perspective of what it means to be a DevOps engineer. Mixing development and operations skills to deploy, manage, monitor, audit, and maintain distributed systems. DevOps is multifaceted and can be compared to glue, in which you’re stitching software, services, databases, Kafka, and more, together to integrate end to end solutions.Using the Confluent Cloud Metrics API (actionable operational metrics), you pull a wide range of metrics about your cluster, a topic or partition, bytes, records, and requests. The Metrics API is unique in that it is queryable. You can send this API question, “What's the max retained bytes per hour over 10 hours for my topic or my cluster?” and find out just like that. To make writing operators much easier, Rick and Allison also share about Crossplane, KUDO, Shell-operator, and how to use these tools.EPISODE LINKSConfluent Cloud Metrics APIShell Operatorkafka-devopsThe Kubernetes Universal Declarative OperatorIntroducing the AWS Controllers for Kubernetes (ACK)Manage any infrastructure your applications need directly from Kubernetes with CrossplaneApache Kafka DevOps with Kubernetes and GitOpsSpring Your Microservices into Production with Kubernetes and GitOpsJoin the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

Apache Kafka® 2.7 is here! Here are the key Kafka Improvement Proposals (KIPs) and updates in this release, presented by Tim Berglund. KIP-497 adds a new inter-broker API to alter in-sync replicas (ISRs). Every partition leader maintains the ISR list or the list of ISRs. KIP-497 is also related to the removal of ZooKeeper.KIP-599 has to do with throttling the rate of creating topics, deleting topics, and creating partitions. This KIP will add a new feature called the controller mutation rate.KIP-612 adds the ability to limit the connection creation rate on brokers, while KIP-651 supports the PEM format for SSL certificates and private keys.The release of Kafka 2.7 furthermore includes end-to-end latency metrics and sliding windows.Find out what’s new with the Kafka broker, producer, and consumer, and what’s new with Kafka Streams in today’s episode of Streaming Audio!EPISODE LINKSRead about what’s new in Apache Kafka 2.7Check out the Apache Kafka 2.7 release notesWatch the video version of this podcastJoin the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

Chris Richardson, creator of the original Cloud Foundry, maintainer of microservices.io and author of “Microservices Patterns,” discovered cloud computing in 2006 during an Amazon talk about APIs for provisioning servers. At this time, you could provision 20 servers and pay 10 cents per hour. This blew his mind and led him in 2008 to create the original Cloud Foundry, a PaaS for deploying Java applications on EC2.One of the original Cloud Foundry’s earliest success stories was a digital marketing agency for a beer company that ran a campaign around the Super Bowl. Cloud Foundry actually enabled them to deploy an application on AWS and then adjust its capacity based on the load. They were leveraging the elasticity of the cloud back in the ‘08–‘09 timeframe. SpringSource eventually acquired Cloud Foundry, followed by VMware. It's the origin of the name of today's Cloud Foundry.Later in the show, Chris explains what choreographed sagas are, reasons to leverage them, and how to measure their efficacy.EPISODE LINKSThe microservices pattern languageEventuate frameworkBook: The Art of ScalabilityUse podcon19 to get 40% off Microservices Patterns by Chris RichardsonJoin the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

We have all heard of Porsche, but did you know that Porsche utilizes event streaming with Apache Kafka®?  Today, Sridhar Mamella (Platform Manager, Data Streaming Platforms, Porsche) discusses how Kafka’s event streaming technology powers Porsche through Streamzilla.With the modern Porsche car having 150–200 sensors, Sridhar dives into what Streamzilla is and how it functions with Kafka on premises and in the cloud. He reveals how the first months of event streaming in production went, Porsche’s path to the cloud, Streamzilla's learnings from a developer and a business perspective, and plans for parts of Streamzilla to go open source.Stick around through the end as Sridhar talks through cloud migration, cloud-first strategy, and Porsche’s event streaming use cases. This Streaming Audio is all about speed—fast cars and fast data, an episode you won't want to miss!EPISODE LINKSWhy Software Is Eating the WorldEvery Company Is Becoming SoftwareTaycan Models at Porsche Join the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

Jason Bell (Apache Kafka® DevOps Engineer, digitalis.io, and Author of “Machine Learning: Hands-On for Developers and Technical Professionals” ) delves into his 32-year journey as a DevOps engineer and how he discovered Apache Kafka. He began his voyage in hardware technology before switching over to software development. From there, he got involved in event streaming in the early 2000s where his love for Kafka started. His first Kafka project involved monitoring Kafka clusters for flight search data, and he's been making magic ever since!Jason first learned about the power of the event streaming during Michael Noll’s talk on the streaming API in 2015. It turned out that Michael had written off 80% of Jason’s streaming API jobs with a single talk. As a Kafka DevOps engineer today, Jason works with on-prem clusters and faces challenges like instant replicas going down and bringing other developers who are new to Kafka up to speed so that they can eventually adopt it and begin building out APIs for Kafka. He shares some tips that have helped him overcome these challenges and bring success to the team.EPISODE LINKSMachine Learning: Hands-On for Developers and Technical Professionals by Jason Bell Join the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

Multi-tenancy has been quite the topic within the Apache Kafka® community. Anna Povzner, an engineer on the Confluent team, spends most of her time working on multi-tenancy in Kafka in Confluent Cloud.Anna kicks off the conversation with Tim Berglund (Senior Director of Developer Experience, Confluent) by explaining what multi-tenancy is, why it is worthy to be desired, and advantages over single-tenant architecture. By putting more applications and use cases on the same Kafka cluster instead of having a separate Kafka cluster for each individual application and use case, multi-tenancy helps minimize the costs of physical machines and also maintenance.She then switches gears to discuss quotas in Kafka. Quotas are essentially limits—you must set quotas for every tenant (or set up defaults) in Kafka. Anna says it’s always best to start with bandwidth quotas because they’re better understood.Stick around until the end as Anna gives us a sneak peek on what’s ahead for multi-tenant Kafka, including KIP-612, the addition of the connection rate quota, which will help protect brokers.EPISODE LINKSSharing is Caring: Toward Creating Self-Tuning Multi-Tenant Kafka (Anna Povzner, Confluent)Join the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

Roger Hoover, one of the first engineers to work on Confluent Cloud, joins Tim Berglund (Senior Director of Developer Experience, Confluent) to chat about the evolution of Confluent Cloud, all the stages that it’s been through, and the lessons he’s learned on the way. He talks through the days before Confluent Platform was created, and how he contributed to Apache Kafka® to run it on OpenStack (the feature used to separate advertised hostnames from the internal hostnames).The Confluent Cloud control plane is now run in over 40 regions. Under the covers, Roger and his team are managing tens of thousands of resources at the cloud provider layer. This means creating VPCs, VMs, volumes, and DNS records, to manage software artifacts, like what version of Kafka is running and user management. Confluent Cloud is a complex application and distributed system spread across the entire world, but Roger reveals how it's done.EPISODE LINKSBuilding Confluent Cloud – Here’s What We’ve Learned Join the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

Do you ever feel like you’re short on time? Well, good news! Confluent Software Engineer Matthias J. Sax is back to discuss how event streaming has changed the game, making time management more simple yet efficient. Matthias explains what watermarking is, the reasons behind why Kafka Streams doesn’t use them, and an alternative approach to watermarking informally called the “slack time approach.” Later, Matthias discusses how you can compare “stream time,” which is the maximum timestamp observed, to the watermark approach as a high-time watermark. Stick around for the end of the episode, where Matthias reveals other new approaches in the pipeline. Learn how to get the most out of your time on today’s episode of Streaming Audio!EPISODE LINKSKafka Summit talk: The Flux Capacitor of Kafka Streams and ksqlDBWatermarks, Tables, Event Time, and the Dataflow ModelKafka Streams’ Take on Watermarks and TriggersJoin the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

What's it like being a distributed systems engineer? Apurva Mehta (Engineering Leader, Confluent) explains what attracted him to Apache Kafka®, the challenges and uniqueness of distributed systems, and how to excel in this industry. He dives into the complex math behind the temporal logic of actions (TLA) and shares about his experiences working at Yahoo and Linkedin, which have prepared him to be where he is today.Apurva also shares what he looks for when hiring someone to join his team. When you're working on a system like Kafka and Kafka Streams, really understanding what your machine is doing, where the bottlenecks are, and how to design improvements to address inefficiencies is critical. EPISODE LINKSJason Gufstason discusses TLA validation (and distributed systems engineering in general) MIT Courseware on Distributed Systems Join the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

It’s Halloween again, which means Anna McDonald (Staff Technical Account Manager, Confluent) is back for another spooktacular episode of Streaming Audio.In this episode, Anna shares six of the most spine-chilling, hair-raising  Apache Kafka® JIRAs from the past year. Her job is to help hunt down problems like these and dig up skeletons like: Early death causes epoch time travelAttack of the clonesMissing snapshot file leads to madnessShrink inWriteLock time to avoid maiming cluster performanceOlder groups are forced to flatlineGhost segment haunts for eternity If JIRAs are undead monsters, Anna is practically a zombie slayer. Get a haunting taste of the horrors that she's battled with as she shares about each of these Kafka updates. Keep calm and scream on in today’s special episode of Streaming Audio!EPISODE LINKSKafka: A Modern Distributed SystemFrom Eager to Smarter in Apache Kafka Consumer Rebalances by Sophie Blee-GoldmanThe Magical Rebalance Protocol of Apache Kafka (Strange Loop)Join the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

It’s the first work-from-home episode of Ask Confluent, where Gwen Shapira (Core Kafka Engineering Leader, Confluent) virtually sits down with Apache Kafka® expert Anna McDonald (Staff Technical Account Manager, Confluent) to answer questions from Twitter. Find out Anna’s favorite Kafka Improvement Proposal (KIP), which  will start to use racially neutral terms in the Kafka community and in our code base, as well as answers to the following questions: If you could pick any one KIP from the backlog that hasn't yet been implemented and have it immediately available, which one would you pick?Are we able to arrive at any formula for identifying the consumer/producer throughput rate in Kafka with the given hardware specifications (CPU, RAM, network, and disk)? Does incremental cooperative rebalancing also work for general Kafka consumers in addition to Kafka Connect rebalancing?They also answer how to determine throughput and achieve your desired SLA by using partitions. EPISODE LINKSWatch Ask Confluent #18: The Toughest Questions ft. Anna McDonaldFrom Eager to Smarter in Apache Kafka Consumer RebalancesStreaming Heterogeneous Databases with Kafka Connect – The Easy WayKeynote: Tim Berglund, Confluent | Closing Keynote Presentation | Kafka Summit 2020Join the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

Wouldn’t it be awesome if there was a language as elegant as Spring Boot is as a framework? In this episode of Streaming Audio, Tim Berglund talks with Josh Long, Spring developer advocate at VMware about Kotlin, about the productivity-focused language from our friends at JetBrains, and how it works with Spring Boot to make the experience leaner, cleaner, and easy to use.Josh shares how the Spring and Kotlin teams have worked hard to make sure that Kotlin and Spring Boot are a first-class experience for all developers trying to get to production faster and safer. They also talk about the issues that arise when wrapping one set of APIs with another, as often arises in the Spring Framework: when APIs should leak, when they should not, and how not to try to be a better Kafka Streams when the original is working well enough. EPISODE LINKSJoin the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

Neil Buesing, an Apache Kafka® community stalwart at Object Partners, spends his days building things out of Kafka and helping others do the same. Today, he discusses the concept of a CoE (center of excellence), and how a CoE is integral to attain and sustain world-class performance, business value, and success in a business. Neil talks us through how to make a CoE successful, the importance of event streaming, how to better understand streaming technologies, and how to best utilize CoE for your needs. This includes evangelizing Kafka, building a Proof of Value (PoV) with team members, defining deliverables as part of that CoE, and understanding how to implement Kafka into your organization. EPISODE LINKSEoS in Kafka: Listen up, I will only say this once! by Jason Gustafson The Magical Rebalance Protocol of Apache Kafka by Gwen Shapira Chair-throwing meme that was discussed at end of episode Apache Kafka and Confluent Platform Reference ArchitectureBenchmark Your Dedicated Apache Kafka Cluster on Confluent CloudOptimizing Your Apache Kafka DeploymentCluster sizingJoin the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

Ever wonder what it's like to intern at a place like Confluent? How about working with Kafka Streams and creating your own KIP? Well, that's exactly what we discuss on today's episode with Leah Thomas. Leah Thomas, who first interned as a recruiter for Confluent, quickly realized that she was enamored with the problem solving the engineering team was doing, especially with Kafka Streams. The next time she joined Confluent's intern program, she worked on the Streams team and helped bring KIP-450 to life. With KIP-450, Leah started learning Apache Kafka® from the inside out and how to better address the user experience. She discusses her experience with getting a KIP approved with the Apache Software Foundation and how she dove into solving the problem of hopping windows with sliding windows instead.EPISODE LINKSRange: How Generalists Triumph in a Specialized WorldConfluent CareersJoin the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

The feature-rich release of Confluent Platform 6.0, based on Apache Kafka® 2.6, introduces Tiered Storage, Self-Balancing Clusters, ksqlDB 0.10, Admin REST APIs, and Cluster Linking in preview. These features enhance the platform with greater elasticity, improved cost effectiveness, infinite data retention, and global availability so that you can simplify management operations, reduce the cost of adopting Kafka, and focus on building event streaming applications.EPISODE LINKSConfluent Platform 6.0 Release NotesIntroducing Confluent Platform 6.0Download Confluent Platform 6.0Watch the video version of this podcastJoin the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)