Confluent Developer ft. Tim Berglund, Adi Polak & Viktor Gamov

Real-time Threat Detection ft. Adi Polak | Ep. 1

The Confluent Developer Podcast is here! For this first episode, Tim Berglund talks to his co-host, Adi Polak (Confluent), about her career in distributed data systems. Her first job: neighborhood dogwalker. Her challenge/theme: working at Akamai with Hadoop on data optimization and real-time threat detection, and the power of collaboration. SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo 🎧 Subscribe to Confluent Developer wherever you listen to podcasts. ▶️ Subscribe on YouTube, and hit the 🔔 to catch new episodes. 👍 If you enjoyed this, please leave us a rating. 🎧 Confluent also has a podcast for tech leaders: "Life Is But A Stream" hosted by our friend, Joseph Morais.

We're back! Welcome to the Confluent Developer Podcast.

Weekly episodes launching Sept. 22! | Hi, I'm Tim Berglund. It's been about four years since I've been podcasting at Confluent, and "Streaming Audio" has been on hiatus for a little more than two, but I've got great news: we are back! We're back with a new name, a new format, and new hosts. Welcome to the Confluent Developer Podcast, where we talk to software developers of all stripes about some of the most interesting problems they've solved in their career. I'll be joined by my co-hosts, Adi Polak and Viktor Gamov. And hey, you know, we're all basically Kafka people, so of course, we're going to gravitate towards experts in data streaming and the technologies relevant in that space. But you know what? We're not limited to that. Really, we want to talk to developers of all kinds about the toughest problems they've solved and how that process changed them and changed the environment around them. So join us. We're launching September 22 with weekly episodes on the Confluent Developer YouTube channel or wherever it is you get your podcasts. We'll see you soon.SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo 🎧 Subscribe to Confluent Developer wherever you listen to podcasts. ▶️ Subscribe on YouTube, and hit the 🔔 to catch new episodes. 👍 If you enjoyed this, please leave us a rating. 🎧 Confluent also has a podcast for tech leaders: "Life Is But A Stream" hosted by our friend, Joseph Morais.

Apache Kafka 3.5 - Kafka Core, Connect, Streams, & Client Updates

Apache Kafka® 3.5 is here with the capability of previewing migrations between ZooKeeper clusters to KRaft mode. Follow along as Danica Fine highlights key release updates.Kafka Core:KIP-833 provides an updated timeline for KRaft.KIP-866 now is preview and allows migration from an existing ZooKeeper cluster to KRaft mode.KIP-900 introduces a way to bootstrap the KRaft controllers with SCRAM credentials.KIP-903 prevents a data loss scenario by preventing replicas with stale broker epochs from joining the ISR list. KIP-915 streamlines the process of downgrading Kafka's transaction and group coordinators by introducing tagged fields.Kafka Connect:KIP-710 provides the option to use a REST API for internal server communication that can be enabled by setting `dedicated.mode.enable.internal.rest` equal to true. KIP-875 offers support for native offset management in Kafka Connect. Connect cluster administrators can now read offsets for both source and sink connectors. This KIP adds a new STOPPED state for connectors, enabling users to shut down connectors and maintain connector configurations without utilizing resources.KIP-894 makes `IncrementalAlterConfigs` API available for use in MirrorMaker 2 (MM2), adding a new use.incremental.alter.config configuration which takes values “requested,” “never,” and “required.”KIP-911 adds a new source tag for metrics generated by the `MirrorSourceConnector` to help monitor mirroring deployments.Kafka Streams:KIP-339 improves Kafka Streams' error-handling capabilities by addressing serialization errors that occur before message production and extending the interface for custom error handling. KIP-889 introduces versioned state stores in Kafka Streams for temporal join semantics in stream-to-table joins. KIP-904 simplifies table aggregation in Kafka by proposing a change in serialization format to enable one-step aggregation and reduce noise from events with old and new keys/values. KIP-914 modifies how versioned state stores are used in Kafka Streams. Versioned state stores may impact different DSL processors in varying ways, see the documentation for details.Kafka Client:KIP-881 is now complete and introduces new client-side assignor logic for rack-aware consumer balancing for Kafka Consumers. KIP-887 adds the `EnvVarConfigProvider` implementation to Kafka so custom configurations stored in environment variables can be injected into the system by providing the map returned by `System.getEnv()`.KIP 641 introduces the `RecordReader` interface to Kafka's clients module, replacing the deprecated MessageReader Scala trait. EPISODE LINKSSee release notes for Apache Kafka 3.5Read the blog to learn moreDownload and get started with Apache Kafka 3.5Watch the video version of this podcast

A Special Announcement from Streaming Audio

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 LINKSTop 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 usage (details)  

How to use Data Contracts for Long-Term Schema Management

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 LINKSOS 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 usage (details)