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

Processing data in real time is a process, as some might say. Angela Chu (Solution Architect, Databricks) and Caio Moreno (Senior Cloud Solution Architect, Microsoft) explain how to integrate Azure, Databricks, and Confluent to build real-time data pipelines that enable you to ingest data, perform analytics, and extract insights from data at hand. They share about where to start within the Apache Kafka® ecosystem and how to maximize the tools and components that it offers using fully managed services like Confluent Cloud for data in motion.

EPISODE LINKS

• Consuming Avro Data from Apache Kafka Topics and Schema Registry with Databricks and Confluent Cloud on Azure 
• Azure Data Lake Storage Gen2 introduction
• Best practices for using Azure Data Lake Storage Gen2
• Join the Confluent Community
• Learn more with Kafka tutorials, resources, and guides at Confluent Developer
• Live demo: Kafka streaming in 10 minutes on Confluent Cloud
• Use 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

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.

Availability in Kafka Streams is hard, especially in the face of any changes. Any change to topic metadata or group membership triggers a rebalance. But Kafka Streams struggles even after this stop-the-world rebalance has finished. According to Apache Kafka® Committer and Confluent Software Engineer Sophie Blee-Goldman, this is because a Streams app will generally have some state associated with a given partition, and to move this state from one consumer instance to another requires rebuilding this state from a special backing topic called a changelog, the source of truth for a partition’s state. 

Restoring the changelog can take hours, and until the state is ready, Streams can’t do any further processing on that partition. Furthermore, it can’t serve any requests for local state until the local state is “caught up” with the changelog. So scaling out your Streams application results in pretty significant downtime—which is a bummer, especially if the reason for scaling out in the first place was to handle a particularly heavy workload.

To solve the stop-the-world rebalance, we have to find a way to safely assign partitions so we can be confident that they’ve been revoked from their previous owner before being given to a new consumer. To solve the scaling out problem in Kafka Streams, we go a step further. When you add a new instance to your Streams application, we won’t immediately assign any stateful partitions to it. Instead, we’ll leave them assigned to their current owner to continue processing and serving queries as usual. During this time, the new instance will start to “warm up” the local state in the background; it starts consuming from the changelog and building up the local state. We then follow a similar pattern as in cooperative rebalancing, and issue a follow-up rebalance. 

In KIP-441, we call these probing rebalances. Every so often (i.e., 10 minutes by default), we trigger a rebalance. In the member’s subscription metadata that it sends to the group leader, each member encodes the current status of its local state. We use the changelog lag as a measure of how “caught up” a partition is. During a rebalance, only instances that are completely caught up are allowed to own stateful tasks; everything else must first warm up the state. So long as there is some task still warming up on a node, we will “probe” with rebalances until it’s ready.

EPISODE LINKS

• From Eager to Smarter in Apache Kafka Consumer Rebalances
• KIP-429: Kafka Consumer Incremental Rebalance Protocol 
• KIP-441: Smooth Scaling Out for Kafka Streams
• Join the Confluent Community
• Learn more with Kafka tutorials, resources, and guides at Confluent Developer
• Live demo: Kafka streaming in 10 minutes on Confluent Cloud
• Use 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

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.

Event-driven architecture has taken on numerous meanings over the years—from event notification to event-carried state transfer, to event sourcing, and CQRS. Why has event-driven programming become so popular, and why is it such a topic of interest? 

For the first time, Simon Aubury (Principal Data Engineer, ThoughtWorks) joins Tim Berglund on the Streaming Audio podcast to tell all, including his own experiences adopting event-driven technologies and common blunders when working in this area.

Simon admits that he’s made some mistakes and learned some valuable lessons that can benefit others. Among these are accidentally building a message bus, the idea that messages are not events, realizing that getting too fixated on the size of a microservice is the wrong problem, the importance of understanding events and boundaries, defining choreography vs. orchestration, and dealing with passive-aggressive events.

This brings Simon to where he is today, as he advocates for Apache Kafka® as a foundation for building a scalable, event-driven architecture and data-intensive applications. 

EPISODE LINKS

• Should You Put Several Event Types in the Same Kafka Topic? 
• Meetup Recording: Event-Driven Architecture Mistakes – I’ve Made a Few
• Join the Confluent Community
• Learn more with Kafka tutorials, resources, and guides at Confluent Developer
• Live demo: Kafka streaming in 10 minutes on Confluent Cloud
• Use 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

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.

Many industries depend on real-time data, requiring a range of solutions that Apache Kafka® can help solve. Samuel Benz (CTO) and Patrick Bönzli (Product Owner) explain how their company, SPOUD, has fully embraced Kafka for data delivery, which has proven to be successful for SPOUD since 2016 across various industries and use cases. 

The four Kafka use cases that Sam and Patrick see most often are microservices, event processing, event sourcing/the data lake, and integration architecture. But implementing streaming software for each of these areas is not without its challenges. It’s easy to become frustrated by trivial problems that arise when integrating Kafka into the enterprise, because it’s not just about technology but also people and how they react to a new technology that they are not yet familiar with. Should enterprises be scared of Kafka? Why can it be hard to adopt Kafka? How do you drive Kafka adoption internally? All good questions.

When adopting Kafka into a new data service, there will be challenges from a data sharing perspective, but with the right architecture, the possibilities are endless. Kafka enables collaboration on previously siloed data in a controlled and layered way. Sam and Patrick’s goal today is to educate others on Kafka and show what success looks like from a data-driven point of view. It’s not always easy, but in the end, event streaming is more than worth it. 

EPISODE LINKS

• Read blog posts from SPOUD
• Improve the Quality of Breaks with Kafka
• Apache Kafka Pyramid
• Ready, Steady, Connect. Help Your Organization to Appreciate Kafka
• AGOORA by SPOUD
• Join the Confluent Community
• Learn more with Kafka tutorials, resources, and guides at Confluent Developer
• Live demo: Kafka streaming in 10 minutes on Confluent Cloud
• Use 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

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.

Synthesis Software Technologies, a Confluent partner, is migrating an existing behavioral IoT framework into Kafka to streamline and normalize vendor information. The legacy messaging technology that they currently use has altered the behavioral IoT data space, and now Apache Kafka® will allow them to take that to the next level. New ways of normalizing the data will allow for increased efficiency for vendors, users, and manufacturers. It will also enable the scaling IoT technology going forward. 

Nick Walker (Principle of Streaming) and Yoni Lew (DevOps Developer) of Synthesis discuss how they utilize Confluent Platform in a personal behavior data pipeline provided by Vitality Group. Vitality Group promotes a shared-value insurance model, which sources behavioral change information and transforms it into personal incentives and rewards to members associated with their global partners.

Yoni shares about the motivators of moving their data from an existing product over to Kafka. The decision was made for two reasons: taking different forms and features of existing data from vendors and streamlining it, and addressing how quickly users of the system want the processed data from the system. Kafka is the best choice for Synthesis because it can stream messages through various topics and workflows while storing them appropriately. It is especially important for Synthesis to be able to replay data as needed without losing its integrity. Yoni explains how Kafka gives them the opportunity to—even if something goes wrong downstream and someone doesn’t process something correctly—process the data on their own timeline and at their rate, because they have the data.

The implementation of Kafka into Synthesis’ current workflow has allowed them to create new functionality for assisting various groups that use the data in different ways. This has furthermore opened up new options for the company to build up its framework using Kafka features that lead to creative reactive applications. With Kafka, Synthesis sees endless opportunities to integrate the data that they collect into usable, historical pipelines for long-term models. 

EPISODE LINKS

• Join the Confluent Community
• Learn more with Kafka tutorials, resources, and guides at Confluent Developer
• Live demo: Kafka streaming in 10 minutes on Confluent Cloud
• Use 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

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.

When it comes to adopting event-driven architectures, a couple of key considerations often arise: the way that an asynchronous core interacts with external synchronous systems and the question of “how do I refactor my monolith into services?” Daniel Jagielski, a consultant working as a tech lead/dev manager at VirtusLab for Tesco, recounts how these very themes emerged in his work with European clients. 

Through observing organizations as they pivot toward becoming real time and event driven, Daniel identifies the benefits of using Apache Kafka® and stream processing for auditing, integration, pub/sub, and event streaming.

He describes the differences between a provisioned cluster vs. managed cluster and the importance of this within the Kafka ecosystem. Daniel also dives into the risk detection platform used by Tesco, which he helped build as a VirtusLab consultant and that marries the asynchronous and synchronous worlds.

As Tesco migrated from a legacy platform to event streaming, determining risk and anomaly detection patterns have become more important than ever. They need the flexibility to adjust due to changing usage patterns with COVID-19. In this episode, Daniel talks integrations with third parties, push-based actions, and materialized views/projects for APIs.

Daniel is a tech lead/dev manager, but he’s also an individual contributor for the Apollo project (an ICE organization) focused on online music usage processing. This means working with data in motion; breaking the monolith (starting with a proof of concept); ETL migration to stream processing, and ingestion via multiple processes that run in parallel with record-level processing.

EPISODE LINKS

• Building an Apache Kafka Center of Excellence Within Your Organization ft. Neil Buesing 
• Risk Management in Retail with Stream Processing
• Event Sourcing, Stream Processing and Serverless
• It’s Time for Streaming to Have a Maturity Model ft. Nick Dearden
• Read Daniel Jagielski's articles on the Confluent blog
• Join the Confluent Community
• Learn more with Kafka tutorials, resources, and guides at Confluent Developer
• Live demo: Kafka streaming in 10 minutes on Confluent Cloud
• Use 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

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.

Viktor Gamov (Developer Advocate, Confluent) joins Tim Berglund on this episode to talk all about Spring and Apache Kafka®. Viktor’s main focus lately has been helping developers build their apps with stream processing, and helping them do this effectively with different languages. Viktor recently hosted an online Spring Boot workshop that turned out to be a lot of fun. This means it was time to get him back on the show to talk about this all-important framework and how it integrates with Kafka and Kafka Streams. 

Spring Boot enables you to do more with less. Its features offer numerous benefits, making it easy to create standalone, production-grade Spring-based applications that you can just run.  The pattern also runs inside the Spring framework for a long time. The Spring Integration Framework implements many enterprise integration patterns and also has a pre-built Kafka connector.

Spring Boot was highly inspired by a 12-factor app manifesto that allows you to write portable apps and extract the configuration, providing different profiles for you to customize your deployment. 

This is a critical part of the Kafka client infrastructure. Even though it's a Java client, Confluent offers a native Spring for Apache Kafka integration for the configuration as a springboard inside Confluent Cloud. If you try to connect your application, you can copy a snippet and place it directly to your Spring application, which works with Confluent Cloud. Now, he’s working on bringing Spring Cloud Stream like YAML-based configuration into Confluent Cloud too so folks can easily copy and paste to work out of the box.

To close, Viktor shares about an interesting new project that the Confluent Developer Relations team is working on. Stick around to hear all about it and learn how Spring and Kafka work together.

EPISODE LINKS

• Spring example in streaming-ops
• Avro, Protobuf, Spring Boot, Kafka Streams and Confluent Cloud | Livestreams
• Event-Driven Microservices with Spring Boot and Confluent Cloud | Livestreams 
• Choosing Christmas Movies with Kubernetes, Spring Boot, and Apache Kafka | Livestreams 015
• Spring Cloud Stream and Confluent Cloud | Livestreams 018
• Joining Forces with Spring Boot, Apache Kafka, and Kotlin ft. Josh Long
• Mastering DevOps with Apache Kafka, Kubernetes, and Confluent Cloud ft. Rick Spurgeon and Allison Walther
• Follow Viktor Gamov on Twitter
• Join the Confluent Community Slack
• Learn more with Kafka tutorials, resources, and guides at Confluent Developer
• Live demo: Kafka streaming in 10 minutes on Confluent Cloud
• Use 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

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.

Confluent Platform 6.1 further simplifies management tasks for Apache Kafka® operators. Based on Apache Kafka 2.7, this release provides even higher availability for enterprises who are using Kafka as the central backbone for their business-critical applications. Confluent Platform 6.1 delivers enhancements that reduce the risk of downtime, simplify operations and streamline the user experience, as well as improve visibility and control with centralized management.

EPISODE LINKS

• Check out the release notes
• Read the blog post: Introducing Confluent Platform 6.1
• Download Confluent Platform 6.1
• Watch the video version of this podcast
• Join the Confluent Community
• Learn more with Kafka tutorials, resources, and guides at Confluent Developer
• Live demo: Kafka streaming in 10 minutes on Confluent Cloud
• Use 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

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.

Nationwide Building Society, a financial institution in the United Kingdom with 137 years of history and over 18,000 employees, relies on Apache Kafka® for their event streaming needs. But how did this come to be? In this episode, Tim Berglund talks with Rob Jackson (Principal Architect, Nationwide) about their Kafka adoption journey as they celebrate two years in production. 

Nationwide chose to adopt Kafka as a central part of their information architecture in order to integrate microservices. You can't have them share a database that's design-time coupling, and maybe you tried having them call each other synchronously. There's a little bit too much runtime coupling, leading to the rise of event-driven reactive microservices as a stable and extensible architecture for the next generation.

Nationwide also chose to use Kafka for the following reasons:

• To replace their mortgage sales systems from traditional orchestration style to event-driven designs and choreography-based solutions using microservices in Kafka
• A cost-effective way to scale their mainframe systems with change data capture (CDC)

Rob explains to Tim that now with the adoption of Kafka across other use cases at Nationwide, he no longer needs to ask his team to query their APIs. Kafka has also enabled more choreography-based use cases and the ability to design new applications to create events (pushed into a common/enterprise event hub). Kafka has helped Nationwide eliminate any bottlenecks in the process and speed up production. 

Furthermore, Rob delves into why his team migrated from orchestration to choreography, explaining their differences in depth. When you start building your applications in a choreography-based way, you will find as a byproduct that interesting events are going into Kafka that you didn’t foresee leveraging but that may be useful for the analytics community. In this way, you can truly get the most out of your data. 

EPISODE LINKS

• Case Study: Event Streaming & Real-Time Data in Banking
• Introducing Events and Stream Processing into Nationwide Building Society (Kafka Summit talk)
• Learn more about Nationwide
• Join the Confluent Community
• Check out Kafka tutorials, resources, and guides at Confluent Developer
• Live demo: Kafka streaming in 10 minutes on Confluent Cloud
• Use 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)

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.