Streaming Audio: Apache Kafka® & Real-Time Data

After recording 64 episodes and featuring 58 amazing guests, the Streaming Audio podcast series has amassed over 130,000 plays on YouTube in the last year. We're extremely proud of these achievements and feel that it's time to take a well-deserved break. Streaming Audio will be taking a vacation! We want to express our gratitude to you, our valued listeners, for spending 10,000 hours with us on this incredible journey. Rest assured, we will be back with more episodes! In the meantime, feel free to revisit some of our previous episodes. For instance, you can listen to Anna McDonald share her stories about the worst Apache Kafka® bugs she’s ever seen, or listen to Jun Rao offer his expert advice on running Kafka in production. And who could forget the charming backstory behind Mitch Seymour's Kafka storybook, Gently Down the Stream? These memorable episodes brought us joy, and we're thrilled to have shared them with you. As we reflect on our accomplishments with pride, we also look forward to an exciting future. Until we meet again, happy listening! EPISODE LINKS Top 6 Worst Apache Kafka JIRA BugsRunning Apache Kafka in ProductionLearn How Stream-Processing Works The Simplest Way PossibleWatch the video version of this podcastStreaming Audio Playlist Join the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usagedetails

Have you ever struggled with managing data long term, especially as the schema changes over time? In order to manage and leverage data across an organization, it’s essential to have well-defined guidelines and standards in place around data quality, enforcement, and data transfer. To get started, Abraham Leal (Customer Success Technical Architect, Confluent) suggests that organizations associate their Apache Kafka® data with a data contract (schema). A data contract is an agreement between a service provider and data consumers. It defines the management and intended usage of data within an organization. In this episode, Abraham talks to Kris about how to use data contracts and schema enforcement to ensure long-term data management. When an organization sends and stores critical and valuable data in Kafka, more often than not it would like to leverage that data in various valuable ways for multiple business units. Kafka is particularly suited for this use case, but it can be problematic later on if the governance rules aren’t established up front. With schema registry, evolution is easy due to its robust security guarantees. When managing data pipelines, you can also use GitOps automation features for an extra control layer. It allows you to be creative with topic versioning, upcasting/downcasting the data collected, and adding quality assurance steps at the end of each run to ensure your project remains reliable. Abraham explains that Protobuf and Avro are the best formats to use rather than XML or JSON because they are built to handle schema evolution. In addition, they have a much lower overhead per-record, so you can save bandwidth and data storage costs by adopting them. There’s so much more to consider, but if you are thinking about implementing or integrating with your data quality team, Abraham suggests that you use schema registry heavily from the beginning. If you have more questions, Kris invites you to join the conversation. You can also watch the KOR Financial Current talk Abraham mentions or take Danica Fine’s free course on how to use schema registry on Confluent Developer. EPISODE LINKS OS projectKOR Financial Current TalkThe Key Concepts of Schema RegistrySchema Evolution and CompatibilitySchema Registry Made Simple by Confluent Cloud ft. Magesh NandakumarKris Jenkins’ TwitterWatch the video version of this podcastStreaming Audio Playlist Join the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usagedetails

Can you use Apache Kafka® and Python together? What’s the current state of Python support? And what are the best options to get started? In this episode, Dave Klein joins Kris to talk about all things Kafka and Python: the libraries, the tools, and the pros & cons. He also talks about the new course he just launched to support Python programmers entering the event-streaming world. Dave has been an active member of the Kafka community for many years and noticed that there were a lot of Kafka resources for Java but few for Python. So he decided to create a course to help people get started using Python and Kafka together. Historically, Java has had the most documentation, and people have often missed how good the Python support is for Kafka users. Python and Kafka are an ideal fit for machine learning applications and data engineering in general. Yet there are a lot of use cases for building, streaming, and machine learning pipelines. In fact, someone conducted a survey to find out what languages were most popular in the Kafka community and Python came in second after Java. That’s how Dave got the idea to create a course for newbies. In this course, Dave combines video lectures with code-heavy exercises to give developers a taste of what the code looks like, how to structure it, a preview of the shape of the code, and the structure of the classes and the functions so you can get hands-on practice using the library. He also covers building a producer and a consumer and using the admin client. And, of course, there is a module that covers working with the schemas supported by the Kafka library. Dave explains that Python opens up a world of opportunity and is ripe for expansion. So if you are ready to dive in, head over to developer.confluent.io to learn more about Dave’s course. EPISODE LINKS Getting Started with Python for Apache KafkaIntroduction to Apache Kafka for Python DevelopersBuilding a Python client application for KafkaCoding in MotionBuilding and Designing Events and Event Streams with Apache KafkaWatch the video version of this podcastKris Jenkins’ TwitterStreaming Audio Playlist Join the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usagedetails

In this episode, Kris interviews Doron Porat, Director of Infrastructure at Yotpo, and Liran Yogev, Director of Engineering at ZipRecruiter (formerly at Yotpo), about their experiences and strategies in dealing with data modeling at scale. Yotpo has a vast and active data lake, comprising thousands of datasets that are processed by different engines, primarily Apache Spark™. They wanted to provide users with self-service tools for generating and utilizing data with maximum flexibility, but encountered difficulties, including poor standardization, low data reusability, limited data lineage, and unreliable datasets. The team realized that Yotpo's modeling layer, which defines the structure and relationships of the data, needed to be separated from the execution layer, which defines and processes operations on the data. This separation would give programmers better visibility into data pipelines across all execution engines, storage methods, and formats, as well as more governance control for exploration and automation. To address these issues, they developed YODA, an internal tool that combines excellent developer experience, DBT, Databricks, Airflow, Looker and more, with a strong CI/CD and orchestration layer. Yotpo is a B2B, SaaS e-commerce marketing platform that provides businesses with the necessary tools for accurate customer analytics, remarketing, support messaging, and more. ZipRecruiter is a job site that utilizes AI matching to help businesses find the right candidates for their open roles. EPISODE LINKS Next Gen Data Modeling in the Open Data PlatformData Mesh 101Data Mesh Architecture: A Modern Distributed Data ModelWatch the video version of this podcastKris Jenkins’ TwitterStreaming Audio Playlist Join the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usagedetails

Migrating Apache Kafka® clusters can be challenging, especially when moving large amounts of data while minimizing downtime. Michael Dunn (Solutions Architect, Confluent) has worked in the data space for many years, designing and managing systems to support high-volume applications. He has helped many organizations strategize, design, and implement successful Kafka cluster migrations between different environments. In this episode, Michael shares some tips about Kafka cluster migration with Kris, including the pros and cons of the different tools he recommends. Michael explains that there are many reasons why companies migrate their Kafka clusters. For example, they may want to modernize their platforms, move to a self-hosted cloud server, or consolidate clusters. He tells Kris that creating a plan and selecting the right tool before getting started is critical for reducing downtime and minimizing migration risks. The good news is that a few tools can facilitate moving large amounts of data, topics, schemas, applications, connectors, and everything else from one Apache Kafka cluster to another. Kafka MirrorMaker/MirrorMaker2 (MM2) is a stand-alone tool for copying data between two Kafka clusters. It uses source and sink connectors to replicate topics from a source cluster into the destination cluster. Confluent Replicator allows you to replicate data from one Kafka cluster to another. Replicator is similar to MM2, but the difference is that it’s been battle-tested. Cluster Linking is a powerful tool offered by Confluent that allows you to mirror topics from an Apache Kafka 2.4/Confluent Platform 5.4 source cluster to a Confluent Platform 7+ cluster in a read-only state, and is available as a fully-managed service in Confluent Cloud. At the end of the day, Michael stresses that coupled with a well-thought-out strategy and the right tool, Kafka cluster migration can be relatively painless. Following his advice, you should be able to keep your system healthy and stable before and after the migration is complete. EPISODE LINKS MirrorMaker 2ReplicatorCluster LinkingSchema MigrationMulti-Cluster Apache Kafka with Cluster LinkingWatch the video version of this podcastKris Jenkins’ TwitterStreaming Audio Playlist Join the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usagedetails

dbt is known as being part of the Modern Data Stack for ELT processes. Being in the MDS, dbt Labs believes in having the best of breed for every part of the stack. Oftentimes folks are using an EL tool like Fivetran to pull data from the database into the warehouse, then using dbt to manage the transformations in the warehouse. Analysts can then build dashboards on top of that data, or execute tests. It’s possible for an analyst to adapt this process for use with a microservice application using Apache Kafka® and the same method to pull batch data out of each and every database; however, in this episode, Amy Chen (Partner Engineering Manager, dbt Labs) tells Kris about a better way forward for analysts willing to adopt the streaming mindset: Reusable pipelines using dbt models that immediately pull events into the warehouse and materialize as materialized views by default. dbt Labs is the company that makes and maintains dbt. dbt Core is the open-source data transformation framework that allows data teams to operate with software engineering’s best practices. dbt Cloud is the fastest and most reliable way to deploy dbt. Inside the world of event streaming, there is a push to expand data access beyond the programmers writing the code, and towards everyone involved in the business. Over at dbt Labs they’re attempting something of the reverse— to get data analysts to adopt the best practices of software engineers, and more recently, of streaming programmers. They’re improving the process of building data pipelines while empowering businesses to bring more contributors into the analytics process, with an easy to deploy, easy to maintain platform. It offers version control to analysts who traditionally don’t have access to git, along with the ability to easily automate testing, all in the same place. In this episode, Kris and Amy explore: EPISODE LINKS dbt labsAn Analytics Engineer’s Guide to StreamingIf Streaming Is the Answer, Why Are We Still Doing Batch?All Current 2022 sessions and slidesWatch the video version of this podcastKris Jenkins’ TwitterStreaming Audio Playlist Join the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usagedetails

What’s the next big thing in the future of streaming data? In this episode, Greg DeMichillie (VP of Product and Solutions Marketing, Confluent) talks to Kris about the future of stream processing in environments where the value of data lies in their ability to intercept and interpret data. Greg explains that organizations typically focus on the infrastructure containers themselves, and not on the thousands of data connections that form within. When they finally realize that they don't have a way to manage the complexity of these connections, a new problem arises: how do they approach managing such complexity? That’s where Confluent and Apache Kafka® come into play - they offer a consistent way to organize this seemingly endless web of data so they don't have to face the daunting task of figuring out how to connect their shopping portals or jump through hoops trying different ETL tools on various systems. As more companies seek ways to manage this data, they are asking some basic questions: The next question for companies who have already adopted Kafka is a bit more complex: "What about my partners?” For example, companies with inventory management systems use supply chain systems to track product creation and shipping. As a result, they need to decide which emails to update, if they need to write custom REST APIs to sit in front of Kafka topics, etc. Advanced use cases like this raise additional questions about data governance, security, data policy, and PII, forcing companies to think differently about data. Greg predicts this is the next big frontier as more companies adopt Kafka internally. And because they will have to think less about where the data is stored and more about how data moves, they will have to solve problems to make managing all that data easier. If you're an enthusiast of real-time data streaming, Greg invites you to attend the Kafka Summit (London) in May and Current (Austin, TX) for a deeper dive into the world of Apache Kafka-related topics now and beyond. EPISODE LINKS What’s Ahead of the Future of Data Streaming?If Streaming Is the Answer, Why Are We Still Doing Batch?All Current 2022 sessions and slidesKafka Summit London 2023Current 2023Watch the video version of this podcastKris Jenkins’ TwitterStreaming Audio Playlist Join the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usagedetails

What can online gaming teach us about making large-scale event management more collaborative in real-time? Ben Gamble (Developer Relations Manager, Aiven) has come to the world of real-time event streaming from an usual source: the video games industry. And if you stop to think about it, modern online games are complex, distributed real-time data systems with decades of innovative techniques to teach us. In this episode, Ben talks with Kris about integrating gaming concepts with Apache Kafka®. Using Kafka’s state management stream processing, Ben has built systems that can handle real-time event processing at a massive scale, including interesting approaches to conflict resolution and collaboration. Building latency into a system is one way to mask data processing time. Ben says that you can efficiently hide latency issues and prioritize performance improvements by setting an initial target and then optimizing from there. If you measure before optimizing, you can add an extra layer to manage user expectations better. Tricks like adding a visual progress bar give the appearance of progress but actually hide latency and improve the overall user experience. To effectively handle challenging activities, like resolving conflicts and atomic edits, Ben suggests “slicing” (or nano batching) to break down tasks into small, related chunks. Slicing allows each task to be evaluated separately, thus producing timely outcomes that resolve potential background conflicts without the user knowing. Ben also explains how he uses pooling to make collaboration seamless. Pooling is a process that links open requests with potential matches. Similar to booking seats on an airplane, seats are assigned when requests are made. As these types of connections are handled through a Kafka event stream, the initial open requests are eventually fulfilled when seats become available. According to Ben, real-world tools that facilitate collaboration (such as Google Docs and Slack) work similarly. Just like multi-player gaming systems, multiple users can comment or chat in real-time and users perceive instant responses because of the techniques ported over from the gaming world. As Ben sees it, the proliferation of these types of concepts across disciplines will also benefit a more significant number of collaborative systems. Despite being long established for gamers, these patterns can be implemented in more business applications to improve the user experience significantly. EPISODE LINKS Going Multiplayer With KafkaBuilding a Dependable Real-Time Betting App with Confluent Cloud and AblyEvent Streaming PatternsWatch the video version of this podcastKris Jenkins’ TwitterStreaming Audio Playlist Join the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usagedetails

Apache Kafka® 3.4 is released! In this special episode, Danica Fine (Senior Developer Advocate, Confluent), shares highlights of the Apache Kafka 3.4 release. This release introduces new KIPs in Kafka Core, Kafka Streams, and Kafka Connect. In Kafka Core: KIP-792KIP-830KIP-854KIP-866 (early access)KIP-876KIP-881In Kafka Streams: KIP-770KIP-837And finally, for Kafka Connect: KIP-787Tune in to learn more about the Apache Kafka 3.4 release! EPISODE LINKS See release notes for Apache Kafka 3.4Read the blog to learn moreDownload Apache Kafka 3.4get startedWatch the video version of this podcastJoin the Community

How can you use OpenTelemetry to gain insight into your Apache Kafka® event systems? Roman Kolesnev, Staff Customer Innovation Engineer at Confluent, is a member of the Customer Solutions & Innovation Division Labs team working to build business-critical OpenTelemetry applications so companies can see what’s happening inside their data pipelines. In this episode, Roman joins Kris to discuss tracing and monitoring in distributed systems using OpenTelemetry. He talks about how monitoring each step of the process individually is critical to discovering potential delays or bottlenecks before they happen; including keeping track of timestamps, latency information, exceptions, and other data points that could help with troubleshooting. Tracing each request and its journey to completion in Kafka gives companies access to invaluable data that provides insight into system performance and reliability. Furthermore, using this data allows engineers to quickly identify errors or anticipate potential issues before they become significant problems. With greater visibility comes better control over application health - all made possible by OpenTelemetry's unified APIs and services. As described on the OpenTelemetry.io website, "OpenTelemetry is a Cloud Native Computing Foundation incubating project. Formed through a merger of the OpenTracing and OpenCensus projects." It provides a vendor-agnostic way for developers to instrument their applications across different platforms and programming languages while adhering to standard semantic conventions so the traces/information can be streamed to compatible systems following similar specs. By leveraging OpenTelemetry, organizations can ensure their applications and systems are secure and perform optimally. It will quickly become an essential tool for large-scale organizations that need to efficiently process massive amounts of real-time data. With its ability to scale independently, robust analytics capabilities, and powerful monitoring tools, OpenTelemetry is set to become the go-to platform for stream processing in the future. Roman explains that the OpenTelemetry APIs for Kafka are still in development and unavailable for open source. The code is complete and tested but has never run in production. But if you want to learn more about the nuts and bolts, he invites you to connect with him on the Confluent Community Slack channel. You can also check out Monitoring Kafka without instrumentation with eBPF - Antón Rodríguez to learn more about a similar approach for domain monitoring. EPISODE LINKS OpenTelemetry java instrumentationOpenTelemetry collectorDistributed Tracing for Kafka with OpenTelemetryMonitoring Kafka without instrumentation with eBPFKris Jenkins' TwitterWatch the videoJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get $100 of free Confluent Cloud usagedetails

Data democratization allows everyone in an organization to have access to the data they need, and the necessary tools needed to use this data effectively. In short, data democratization enables better business decisions. In this episode, Rama Ryali, a Senior IT and Data Executive, chats with Kris Jenkins about the importance of data democratization in modern systems. Rama explains that tech has unprecedented control over data and ignores basic business needs. Tech’s influence has largely gone unchecked and has led to a disconnect that often forces businesses to hire outside vendors for help turning their data into information they can use. In his role at RightData, Rama worked closely with Marketing, Sales, Customers, and Leadership to develop a no-code unified data platform that is accessible to everyone and fosters data democratization. So what is data democracy anyway? Rama explains that data democratization is the process of making data more accessible and open to a wider audience in a unified, no-code UI. It involves making sure that data is available to people who need it, regardless of their technical expertise or background. This enables businesses to make data-driven decisions faster and reduces the costs associated with acquiring, processing, and storing information. In addition, by allowing more people access to data, organizations can better collaborate and access tools that allow them to gain valuable insights into their operations and gain a competitive edge in the marketplace. In a perfect world, complicated tools supported by SQL, Excel, etc., with static views of data, will be replaced by a UI that anyone can use to analyze real-time streaming data. Kris coined a phase, “data socialization,” which describes the way that these types of tools can enable human connections across all areas of the organization, not just tech. Rama acknowledges that Excel, SQL, and other dev-heavy platforms will never go away, but the future of data democracy will allow businesses to unlock the maximum value of data through an iterative, democratic process where people talk about what the data is, what matters to other people, and how to transmit it in a way that makes sense. EPISODE LINKS RightData LinkedInThe 5 W’s of Metadata by Rama RyaliReal-Time Machine Learning and Smarter AI with Data StreamingWatch the video version of this podcastKris Jenkins’ TwitterStreaming Audio Playlist Join the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usagedetails

Is it possible to manage and test data like code? lakeFS is an open-source data version control tool that transforms object storage into Git-like repositories, offering teams a way to use the same workflows for code and data. In this episode, Kris sits down with guest Adi Polak, VP of DevX at Treeverse, to discuss how lakeFS can be used to facilitate better management and testing of data. At its core, lakeFS provides teams with better data management. A theoretical data engineer on a large team runs a script to delete some data, but a bug in the script accidentally deletes a lot more data than intended. Application engineers can checkout the main branch, effectively erasing their mistakes, but without a tool like lakeFS, this data engineer would be in a lot of trouble. Polak is quick to explain that lakeFS isn’t built on Git. The source code behind an application is usually a few dozen mega bytes, while lakeFS is designed to handle petabytes of data; however, it does use Git-like semantics to create and access versions so adoption is quick and simple. Another big challenge that lakeFS helps teams tackle is reproducibility. Troubleshooting when and where a corruption in the data first appeared can be a tricky task for a data engineer, when data is constantly updating. With lakeFS, engineers can refer to snapshots to see where the product was corrupted, and rollback to that exact state. lakeFS also assists teams with reprocessing of historical data. With lakeFS data can be reprocessed on an isolated branch, before merging, to ensure the reprocessed data is exposed atomically. It also makes it easier to access the different versions of reprocessed data using any tag or a historical commit ID. Tune in to hear more about the benefits of lakeFS. EPISODE LINKS Adi Polak's TwitterlakeFS Git-for-data What is a Merkle Tree?If Streaming Is the Answer, Why Are We Still Doing Batch?Current 2022 sessions and slidesSign up for updates on Current 2023Watch the video version of this podcastKris Jenkins’ TwitterStreaming Audio Playlist Join the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usagedetails

How does leader election work in Apache Kafka®? For the past 2 ½ years, Adithya Chandra, Staff Software Engineer at Confluent, has been working on Kafka scalability and performance, specifically partition leader election. In this episode, he gives Kris Jenkins a deep dive into the power of leader election in Kafka replication, why we need it, how it works, what can go wrong, and how it's being improved. Adithya explains that you can configure a certain number of replicas to be distributed across Kafka brokers and then set one of them as the elected leader - the others become followers. This leader-based model proves efficient because clients only have to write to the leader, who handles the replication process internally. But what happens when a broker goes offline, when a replica reassignment occurs, or when a broker shuts down? Adithya explains that when these triggers occur, one of the followers becomes the elected leader, and all the other replicas take their cue from the new leader. This failover reassignment ensures that messages are replicated effectively and efficiently with multiple copies across different brokers. Adithya explains how you can select a broker as the preferred election leader. The preferred leader then becomes the new leader in failure events. This reduces latency and ensures messages consistently write to the same broker for easier tracking and debugging. Leader failover cannot cover all failures, Adithya says. If a broker can’t be reached externally but can talk to other brokers in the cluster, leader failover won’t be triggered. If a broker experiences transient disk or network issues, the leader election process might fail, and the broker will not be elected as a leader. In both cases, manual intervention is required. Leadership priority is an important feature of Confluent Cloud that allows you to prioritize certain brokers over others and specify which broker is most likely to become the leader in case of a failover. This way, we can prioritize certain brokers to ensure that the most reliable broker handles more important and sensitive replication tasks. Additionally, this feature ensures that replication remains consistent and available even in an unexpected failure event. Improvements to this component of Kafka will enable it to be applied to a wide variety of scenarios. On-call engineers can use it to mitigate single-broker performance issues while debugging. Network and storage health solutions can use it to prioritize brokers. Adithya explains that preferred leader election and leadership failover ensure data is available and consistent during failure scenarios so that Kafka replication can run smoothly and efficiently. EPISODE LINKS Data Plane: Replication ProtocolOptimizing Cloud-Native Apache Kafka Performance ft. Alok Nikhil and Adithya ChandraWatch the videoKris Jenkins’ TwitterJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usagedetails

Are bad customer experiences really just data integration problems? Can real-time data streaming and machine learning be democratized in order to deliver a better customer experience? Airy, an open-source data-streaming platform, uses Apache Kafka® to help business teams deliver better results to their customers. In this episode, Airy CEO and co-founder Steffen Hoellinger explains how his company is expanding the reach of stream-processing tools and ideas beyond the world of programmers. Airy originally built Conversational AI (chatbot) software and other customer support products for companies to engage with their customers in conversational interfaces. Asynchronous messaging created a large amount of traffic, so the company adopted Kafka to ingest and process all messages & events in real time. In 2020, the co-founders decided to open source the technology, positioning Airy as an open source app framework for conversational teams at large enterprises to ingest and process conversational and customer data in real time. The decision was rooted in their belief that all bad customer experiences are really data integration problems, especially at large enterprises where data often is siloed and not accessible to machine learning models and human agents in real time. (Who hasn’t had the experience of entering customer data into an automated system, only to have the same data requested eventually by a human agent?) Airy is making data streaming universally accessible by supplying its clients with real-time data and offering integrations with standard business software. For engineering teams, Airy can reduce development time and increase the robustness of solutions they build. Data is now the cornerstone of most successful businesses, and real-time use cases are becoming more and more important. Open-source app frameworks like Airy are poised to drive massive adoption of event streaming over the years to come, across companies of all sizes, and maybe, eventually, down to consumers. EPISODE LINKS Learn how to deploy Airy Open Source - or sign up for an Airy Cloud test instanceGoogle Case Study about Airy & TEDi, a 2,000 store retailerBecome an Expert in Conversational EngineeringSupercharging conversational AI with human agent feedback loopsIntegrating all Communication and Customer Data with Airy and ConfluentHow to Build and Deploy Scalable Machine Learning in Production with Apache KafkaReal-Time Threat Detection Using Machine Learning and Apache KafkaWatch the videoLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get $100 of free Confluent Cloud usagedetails

The past year saw new trends emerge in the world of data streaming technologies, as well as some unexpected and novel use cases for Apache Kafka®. New reflections on the future of stream processing and when companies should adopt microservice architecture inspired several talks at this year’s industry conferences. In this episode, Kris is joined by his colleagues Danica Fine, Senior Developer Advocate, and Robin Moffatt, Principal Developer Advocate, for an end-of-year roundtable on this year’s developments and what they want to see in the year to come. Robin and Danica kick things off with a discussion of the year’s memorable conferences. Talk submissions for Kafka Summit London and Current 2022 featuring topics were noticeably more varied than previous years, with fewer talks focused on the basics of Kafka implementation. Many abstracts featured interesting and unusual use cases, in addition to detailed explanations on what went wrong and how others could avoid the same issues. The conferences also made clear that a lot of companies are adopting or considering stream-processing solutions. Are we close to a future where streaming is a part of everything we do? Is there anything helping streaming become more mainstream? Will stream processing replace batch? On the other hand, a lot of in-demand talks focused on the importance of understanding the best practices supporting data mesh and understanding the nuances of the system and configurations. Danica identifies this as her big hope for next year: No more Kafka developers pursuing quick fixes. “No more band aid fixes. I want as many people as possible to understand the nuances of the levers that they're pulling for Kafka, whatever project they're building.” Kris and Robin agree that what will make them happy in 2023 is seeing broader, more diverse client libraries for Kafka. “Getting away from this idea that Kafka is largely a Java shop, which is nonsense, but there is that perception.” Streaming Audio returns in January 2023. EPISODE LINKS Put Your Data To Work: Top 5 Data Technology Trends for 2023Write What You Know: Turning Your Apache Kafka Knowledge into a Technical TalkCommon Apache Kafka Mistakes to AvoidPractical Data Pipeline: Build a Plant Monitoring System with ksqlDBIf Streaming Is the Answer, Why Are We Still Doing Batch?View sessions and slides from Current 2022Watch the video version of this podcastKris Jenkins’ TwitterStreaming Audio Playlist Join the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usagedetails

Entomophiliac, Anna McDonald (Principal Customer Success Technical Architect, Confluent) has seen her fair share of Apache Kafka® bugs. For her annual holiday roundup of the most noteworthy Kafka bugs, Anna tells Kris Jenkins about some of the scariest, most surprising, and most enlightening corner cases that make you ask, “Ah, so that’s how it really works?” She shares a lot of interesting details about how batching works, the replication protocol, how Kafka’s networking stack dances with Linux’s one, and which is the most important Scala class to read, if you’re only going to read one. In particular, Anna gives Kris details about a bug that he’s been thinking about lately – sticky partitioner (KAFKA-10888). When a Kafka producer sends several records to the same partition at around the same time, the partition can get overloaded. As a result, if too many records get processed at once, they can get stuck causing an unbalanced workload. Anna goes on to explain that the fix required keeping track of the number of offsets/messages written to each partition, and then batching to force more balanced distributions. She found another bug that occurs when Kafka server triggers TCP Congestion Control in some conditions (KAFKA-9648). Anna explains that when Kafka server restarts and then executes the preferred replica leader, lots of replica leaders trigger cluster metadata updates. Then, all clients establish a server connection at the same time that lots TCP requests are waiting in the TCP sync queue. The third bug she talks about (KAFKA-9211), may cause TCP delays after upgrading…. Oh, that’s a nasty one. She goes on to tell Kris about a rare bug (KAFKA-12686) in Partition.scala where there’s a race condition between the handling of an AlterIsrResponse and a LeaderAndIsrRequest. This rare scenario involves the delay of AlterIsrResponse when lots of ISR and leadership changes occur due to broker restarts. Bugs five (KAFKA-12964) and six (KAFKA-14334) are no better, but you’ll have to plug in your headphones and listen in to explore the ghoulish adventures of Anna McDonald as she gives a nightmarish peek into her world of JIRA bugs. It’s just what you might need this holiday season! EPISODE LINKS KAFKA-10888: Sticky partition leads to uneven product msg, resulting in abnormal delays in some partitionsKAFKA-9648: Add configuration to adjust listen backlog size for AcceptorKAFKA-9211: Kafka upgrade 2.3.0 may cause tcp delay ack(Congestion Control)KAFKA-12686: Race condition in AlterIsr response handlingKAFKA-12964: Corrupt segment recovery can delete new producer state snapshotsKAFKA-14334: DelayedFetch purgatory not completed when appending as followerOptimizing for Low Latency and High ThroughputDiagnose and Debug Apache Kafka IssuesWatch the videoJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperUse PODCAST100 to get $100 of free Confluent Cloud usagedetails

Could you explain Apache Kafka® in ways that a small child could understand? When Mitch Seymour, author of Mastering Kafka Streams and ksqlDB, wanted a way to communicate the basics of Kafka and event-based stream processing, he decided to author a children’s book on the subject, but it turned into something with a far broader appeal. Mitch conceived the idea while writing a traditional manuscript for engineers and technicians interested in building stream processing applications. He wished he could explain what he was writing about to his 2-year-old daughter, and contemplated the best way to introduce the concepts in a way anyone could grasp. Four months later, he had completed the illustration book: Gently Down the Stream: A Gentle Introduction to Apache Kafka. It tells the story of a family of forest-dwelling Otters, who discover that they can use a giant river to communicate with each other. When more Otter families move into the forest, they must learn to adapt their system to handle the increase in activity. This accessible metaphor for how streaming applications work is accompanied by Mitch’s warm, painterly illustrations. For his second book, Seymour collaborated with the researcher and software developer Martin Kleppmann, author of Designing Data-Intensive Applications. Kleppmann admired the illustration book and proposed that the next book tackle a gentle introduction to cryptography. Specifically, it would introduce the concepts behind symmetric-key encryption, key exchange protocols, and the Diffie-Hellman algorithm, a method for exchanging secret information over a public channel. Secret Colors tells the story of a pair of Bunnies preparing to attend a school dance, who eagerly exchange notes on potential dates. They realize they need a way of keeping their messages secret, so they develop a technique that allows them to communicate without any chance of other Bunnies intercepting their messages. Mitch’s latest illustration book is—A Walk to the Cloud: A Gentle Introduction to Fully Managed Environments. In the episode, Seymour discusses his process of creating the books from concept to completion, the decision to create his own publishing company to distribute these books, and whether a fourth book is on the way. He also discusses the experience of illustrating the books side by side with his wife, shares his insights on how editing is similar to coding, and explains why a concise set of commands is equally desirable in SQL queries and children’s literature. EPISODE LINKS Minimizing Software Speciation with ksqlDB and Kafka StreamsGently Down the Stream: A Gentle Introduction to Apache KafkaSecret ColorsA Walk to the Cloud: A Gentle Introduction to Fully Managed EnvironmentsApache Kafka On the Go: Kafka Concepts for BeginnersApache Kafka 101 courseWatch the videoJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperUse PODCAST100 to get an additional $100 of free Confluent Cloud usagedetails

What are the key factors to consider when developing event-driven architecture? When properly designed, events can connect existing systems with a common language and allow data exchange in near real time. They also help reduce complexity by providing a single source of truth that eliminates the need to synchronize data between different services or applications. They enable dynamic behavior, allowing each service or application to respond quickly to changes in its environment. Using events, developers can create systems that are more reliable, responsive, and easier to maintain. In this podcast, Adam Bellemare, staff technologist at Confluent, discusses the four dimensions of events and designing event streams along with best practices, and an overview of a new course he just authored. This course, called Introduction to Designing Events and Event Streams, walks you through the process of properly designing events and event streams in any event-driven architecture. Adam explains that the goal of the course is to provide you with a foundation for designing events and event streams. Along with hands-on exercises and best practices, the course explores the four dimensions of events and event stream design and applies them to real-world problems. Most importantly, he talks to Kris about the key factors to consider when deciding what events to write, what events to publish, and how to structure and design them to trigger actions like broadcasting messages to other services or storing results in a database. How you design and implement events and event streams significantly affect not only what you can do today, but how you scale in the future. Head over to Introduction to Designing Events and Event Streams to learn everything you need to know about building an event-driven architecture. EPISODE LINKS Introduction to Designing Events and Event StreamsPractical Data Mesh: Building Decentralized Data Architecture with Event StreamsThe Data Dichotomy: Rethinking the Way We Treat Data and ServicesCoding in Motion: Sound & Vision—Build a Data Streaming App with JavaScript and Confluent CloudUsing Event-Driven Design with Apache Kafka Streaming Applications ft. Bobby CalderwoodWatch the video version of this podcastKris Jenkins’ TwitterStreaming Audio Playlist Join the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usagedetails

Is there a better way to manage access to resources without compromising security? New employees need access to a variety of resources within a company's tech stack. But manually granting access can be error-prone. And when employees leave, their access must be revoked, thus potentially introducing security risks if an admin misses one. In this podcast, Kris Jenkins talks to Anuj Sawani (Security Product Manager, Confluent) about the centralized identity management system he helped build to integrate with Apache Kafka® to prevent common identity management headaches and security risks. With 12+ years of experience building cybersecurity products for enterprise companies, Anuj Sawani explains how he helped build out KIP-768 (Secured OAuth support in Kafka) that supports a unified identity mechanism that spans across cloud and on-premises (hybrid scenarios). Confluent Cloud customers wanted a single identity to access all their services. The manual process required managing different sets of identity stores across the ecosystem. Anuj goes on to explain how Identity and Access Management (IAM) using cloud-native authentication protocols, such as OAuth or OpenID Connect, solves this problem by centralizing identity and minimizing security risks. Anuj emphasizes that sticking with industry standards is key because it makes integrating with other systems easy. With OAuth now supported in Kafka, this means performing client upgrades, configuring identity providers, etc. to ensure the applications can leverage new capabilities. Some examples of how to do this are to use centralized identities for client/broker connections. As Anuj continues to build and enhance features, he hopes to recommend this unified solution to other technology vendors because it makes integration much easier. The goal is to create a web of connectors that support the same standards. The future is bright, as other organizations are researching supporting OAuth and similar industry standards. Anuj is looking forward to the evolution and applying it to other use cases and scenarios. EPISODE LINKS Introduction to Confluent Cloud SecurityKIP-768: Secured OAuth support in Apache KafkaConfluent Cloud Documentation: OAuth 2.0 SupportApache Kafka Security Best PracticesSecurity for Real-Time Data Stream Processing with Confluent CloudWatch the video version of this podcastKris Jenkins’ TwitterStreaming Audio Playlist Join the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usagedetails

Can we use machine learning to detect security threats in real-time? As organizations increasingly rely on distributed systems, it is becoming more important to analyze the traffic that passes through those systems quickly. Confluent Hackathon ’22 finalist, Géraud Dugé de Bernonville (Data Consultant, Zenika Bordeaux), shares how his team used TensorFlow (machine learning) and Neo4j (graph database) to analyze and detect network traffic data in real-time. What started as a research and development exercise turned into ZIEM, a full-blown internal project using ksqlDB to manipulate, export, and visualize data from Apache Kafka®. Géraud and his team noticed that large amounts of data passed through their network, and they were curious to see if they could detect threats as they happened. As a hackathon project, they built ZIEM, a network mapping and intrusion detection platform that quickly generates network diagrams. Using Kafka, the system captures network packets, processes the data in ksqlDB, and uses a Neo4j Sink Connector to send it to a Neo4j instance. Using the Neo4j browser, users can see instant network diagrams showing who's on the network, allowing them to detect anomalies quickly in real time. The Ziem project was initially conceived as an experiment to explore the potential of using Kafka for data processing and manipulation. However, it soon became apparent that there was great potential for broader applications (banking, security, etc.). As a result, the focus shifted to developing a tool for exporting data from Kafka, which is helpful in transforming data for deeper analysis, moving it from one database to another, or creating powerful visualizations. Géraud goes on to talk about how the success of this project has helped them better understand the potential of using Kafka for data processing. Zenika plans to continue working to build a pipeline that can handle more robust visualizations, expose more learning opportunities, and detect patterns. EPISODE LINKS Ziem Project on GitHub ksqlDB 101 courseksqlDB Fundamentals: How Apache Kafka, SQL, and ksqlDB Work together ft. Simon AuburyReal-Time Stream Processing, Monitoring, and Analytics with Apache KafkaApplication Data Streaming with Apache Kafka and SwimWatch the video version of this podcastKris Jenkins’ TwitterStreaming Audio Playlist Join the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usagedetails