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 Streams
• Gently Down the Stream: A Gentle Introduction to Apache Kafka
• Secret Colors
• A Walk to the Cloud: A Gentle Introduction to Fully Managed Environmen

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

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• 🎧 Confluent also has a podcast for tech leaders: "Life Is But A Stream" hosted by our friend, Joseph Morais.

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 Streams
• Practical Data Mesh: Building Decentralized Data Architecture with Event Streams
• The Data Dichotomy: Rethinking the Way We Treat Data and Services
• Coding in Motion: Sound & Vision—Build a Data Streaming App with JavaScript and Confluent Cloud
• Using Event-Driven Design with Apache Kafka Streaming Applications ft. Bobby Calderwood
• Watch the video version of this podcast
• Kris Jenkins’ Twitter
• Streaming Audio Playlist 

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.

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 Security
• KIP-768: Secured OAuth support in Apache Kafka
• Confluent Cloud Documentation: OAuth 2.0 Support
• Apache Kafka Security Best Practices
• Security for Real-Time Data Stream Processing with Confluent Cloud
• Watch the video version of this podcast
• Kris Jenkins’ Twitter
• Streaming Audio Playlis

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.

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 course
• ksqlDB Fundamentals: How Apache Kafka, SQL, and ksqlDB Work together ft. Simon Aubury
• Real-Time Stream Processing, Monitoring, and Analytics with Apache Kafka
• Application Data Streaming with Apache Kafka and Swim
• Watch the video version of this podcast
• Kris Jenkins’ Twitter
• Streaming Audio Playlist 
• Join t

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.

What happens when you need to store more than a few petabytes of data? Rittika Adhikari (Software Engineer, Confluent) discusses how her team implemented tiered storage, a method for improving the scalability and elasticity of data storage in Apache Kafka®. She also explores the motivating factors for building it in the first place: cost, performance, and manageability. 

Before Tiered Storage, there was no real way to retain Kafka data indefinitely. Because of the tight coupling between compute and storage, users were forced to use different tools to access cold and hot data. Additionally, the cost of re-replication was prohibitive because Kafka had to process large amounts of data rather than small hot sets.

As a member of the Kafka Storage Foundations team, Rittika explains to Kris Jenkins how her team initially considered a Kafka data lake but settled on a more cost-effective method – tiered storage. With tiered storage, one tier handles elasticity and throughput for long-term storage, while the other tier is dedicated to high-cost, low-latency, short-term storage. Before, re-replication impacted all brokers, slowing down performance because it required more replication cycles. By decoupling compute and storage, they now only replicate the hot set rather than weeks of data. 

Ultimately, this tiered storage method broke down the barrier between compute and storage by separating data into multiple tiers across the cloud. This allowed for better scalability and elasticity that reduced operational toil. 

In preparation for a broader rollout to customers who heavily rely on compacted topics, Rittika’s team will be implementing tier compaction to support tiering of compacted topics. The goal is to have the partition leader perform compaction. This will substantially reduce compaction costs (CPU/disk) because the number of replicas compacting is significantly smaller. It also protects the broker resource consumption through a new compaction algorithm and throttling. 

EPISODE LINKS

• Jun Rao explains: What is Tiered Storage?
• Enabling Tiered Storage
• Infinite Storage in Confluent Platform
• Kafka Storage and Processing Fundamentals
• KIP-405: Kafka Tiered Storage
• Optimizing Apache Kafka’s Internals with Its Co-Creator Jun Rao
• Watch the video version of this podcast
• Kris Jenkins’ Twitter
• Streaming Audio Playlist 

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.

In principle, data mesh architecture should liberate teams to build their systems and gather data in a distributed way, without having to explicitly coordinate. Data is the thing that can and should decouple teams, but proper implementation has its challenges.

In this episode, Kris talks to Florian Albrecht (Solution Architect, Hermes Germany) about Galapagos, an open-source DevOps software tool for Apache Kafka® that Albrecht created with his team at Hermes, a German parcel delivery company. 

After Hermes chose Kafka to implement company-wide event-driven architecture, Albrecht’s team created rules and guidelines on how to use and really make the most out of Kafka. But the hands-off approach wasn’t leading to greater independence, so Albrecht’s team tried something different to documentation— they encoded the rules as software.

This method pushed the teams to stop thinking in terms of data and to start thinking in terms of events. Previously, applications copied data from one point to another, with slight changes each time. In the end, teams with conflicting data were left asking when the data changed and why, with a real impact on customers who might be left wondering when their parcel was redirected and how. Every application would then have to be checked to find out when exactly the data was changed. Event architecture terminates this cycle. 

Events are immutable and changes are registered as new domain-specific events. Packaged together as event envelopes, they can be safely copied to other applications, and can provide significant insights. No need to check each application to find out when manually entered or imported data was changed—the complete history exists in the event envelope. More importantly, no more time-consuming collaborations where teams help each other to interpret the data. 

Using Galapagos helped the teams at Hermes to switch their thought process from raw data to event-driven. Galapagos also empowers business teams to take charge of their own data needs by providing a protective buffer. When specific teams,  providers of data or events, want to change something, Galapagos enforces a method which will not kill the production applications already reading the data. Teams can add new fields which existing applications can ignore, but a previously required field that an application could be relying on won’t be changeable. 

Business partners using Galapagos found they were better prepared to give answers to their developer colleagues, allowing different parts of the business to communicate in ways they hadn’t before. Through Galapagos, Hermes saw better success decoupling teams.

EPISODE LINKS

• A Guide to Data Mesh
• Practical Data Mesh ebook
• Galapagos GitHub
• Florian Albrecht GitHub
• Watch the video

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.

Is real-time data streaming the future, or will batch processing always be with us? Interest in streaming data architecture is booming, but just as many teams are still happily batching away. Batch processing is still simpler to implement than stream processing, and successfully moving from batch to streaming requires a significant change to a team’s habits and processes, as well as a meaningful upfront investment. Some are even running dbt in micro batches to simulate an effect similar to streaming, without having to make the full transition. Will streaming ever fully take over?

In this episode, Kris talks to a panel of industry experts with decades of experience building and implementing data systems. They discuss the state of streaming adoption today, if streaming will ever fully replace batch, and whether it even could (or should). Is micro batching the natural stepping stone between batch and streaming? Will there ever be a unified understanding on how data should be processed over time? Is the lack of agreement on best practices for data streaming an insurmountable obstacle to widespread adoption? What exactly is holding teams back from fully adopting a streaming model?

Recorded live at Current 2022: The Next Generation of Kafka Summit, the panel includes Adi Polak (Vice President of Developer Experience, Treeverse), Amy Chen (Partner Engineering Manager, dbt Labs), Eric Sammer (CEO, Decodable), and Tyler Akidau (Principal Software Engineer, Snowflake).

EPISODE LINKS

• dbt Labs
• Decodable
• lakeFS
• Snowflake
• View sessions and slides from Current 2022
• Stream Processing vs. Batch Processing: What to Know
• From Batch to Real-Time: Tips for Streaming Data Pipelines with Apache Kafka ft. Danica Fine
• Watch the video version of this podcast
• Kris Jenkins’ Twitter
• Streaming Audio Playlist 
• Join the Confluent Community
• Learn more with Kafka tutorials, resources, and guides at Confluent Developer
• Live demo: Intro to Event-Driven Microservices with Confluent
• Use PODCAST100 to get an additional $100 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.

Streaming real-time data at scale and processing it efficiently is critical to cybersecurity organizations like SecurityScorecard. Jared Smith, Senior Director of Threat Intelligence, and Brandon Brown, Senior Staff Software Engineer, Data Platform at SecurityScorecard, discuss their journey from using RabbitMQ to open-source Apache Kafka® for stream processing. As well as why turning to fully-managed Kafka on Confluent Cloud is the right choice for building real-time data pipelines at scale.

SecurityScorecard mines data from dozens of digital sources to discover security risks and flaws with the potential to expose their client’ data. This includes scanning and ingesting data from a large number of ports to identify suspicious IP addresses, exposed servers, out-of-date endpoints, malware-infected devices, and other potential cyber threats for more than 12 million companies worldwide.

To allow real-time stream processing for the organization, the team moved away from using RabbitMQ to open-source Kafka for processing a massive amount of data in a matter of milliseconds, instead of weeks or months. This makes the detection of a website’s security posture risk happen quickly for constantly evolving security threats. The team relied on batch pipelines to push data to and from Amazon S3 as well as expensive REST API based communication carrying data between systems. They also spent significant time and resources on open-source Kafka upgrades on Amazon MSK.

Self-maintaining the Kafka infrastructure increased operational overhead with escalating costs. In order to scale faster, govern data better, and ultimately lower the total cost of ownership (TOC), Brandon, lead of the organization’s Pipeline team, pivoted towards a fully-managed, cloud-native approach for more scalable streaming data pipelines, and for the development of a new Automatic Vendor Detection (AVD) product.

Jared and Brandon continue to leverage the Cloud for use cases including using PostgreSQL and pushing data to downstream systems using CSC connectors, increasing data governance and security for streaming scalability, and more.

EPISODE LINKS

• SecurityScorecard Case Study
• Building Data Pipelines with Apache Kafka and Confluent
• Watch the video version of this podcast
• Kris Jenkins’ Twitter
• Streaming Audio Playlist 
• Join the Confluent Community
• Learn more with Kafka tutorials, resources, and guides at Confluent Developer
• Live demo: Intro to Event-Driven Microservices with Confluent

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.

What are some recommendations to consider when running Apache Kafka® in production? Jun Rao, one of the original Kafka creators, as well as an ongoing committer and PMC member, shares the essential wisdom he's gained from developing Kafka and dealing with a large number of Kafka use cases.

Here are 6 recommendations for maximizing Kafka in production:

1. Nail Down the Operational Part
When setting up your cluster, in addition to dealing with the usual architectural issues, make sure to also invest time into alerting, monitoring, logging, and other operational concerns. Managing a distributed system can be tricky and you have to make sure that all of its parts are healthy together.  This will give you a chance at catching cluster problems early, rather than after they have become full-blown crises.

2. Reason Properly About Serialization and Schemas Up Front
At the Kafka API level, events are just bytes, which gives your application the flexibility to use various serialization mechanisms. Avro has the benefit of decoupling schemas from data serialization, whereas Protobuf is often preferable to those practiced with remote procedure calls; JSON Schema is user friendly but verbose. When you are choosing your serialization, it's a good time to reason about schemas, which should be well-thought-out contracts between your publishers and subscribers. You should know who owns a schema as well as the path for evolving that schema over time.

3. Use Kafka As a Central Nervous System Rather Than As a Single Cluster
Teams typically start out with a single, independent Kafka cluster, but they could benefit, even from the outset, by thinking of Kafka more as a central nervous system that they can use to connect disparate data sources. This enables data to be shared among more applications.

4. Utilize Dead Letter Queues (DLQs)
DLQs can keep service delays from blocking the processing of your messages. For example, instead of using a unique topic for each customer to which you need to send data (potentially millions of topics),  you may prefer to use a shared topic, or a series of shared topics that contain all of your customers. But if you are sending to multiple customers from a shared topic and one customer's REST API is down—instead of delaying the process entirely—you can have that customer's events divert into a dead letter queue. You can then process them later from that queue.

5. Understand Compacted Topics
By default in Kafka topics, data is kept by time. But there is also another type of topic, a compacted topic, which stores data by key and replaces old data with new data as it comes in. This is particularly useful for working with data that is updateable, for example, data that may be coming in through a change-data-capture log. A practical example of this would be a retailer that needs to update prices and product descriptions to send out to all of its locations.

6. Imagine New Use Cases Enabled by Kafka's Recent Evolution
The biggest recent change in Kafka's history is its migration to the cloud. By using Kafka there, you can reserve you

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.

Is it possible to build a real-time data platform without using stateful stream processing? Forecasty.ai is an artificial intelligence platform for forecasting commodity prices, imparting insights into the future valuations of raw materials for users. Nearly all AI models are batch-trained once, but precious commodities are linked to ever-fluctuating global financial markets, which require real-time insights. In this episode, Ralph Debusmann (CTO, Forecasty.ai) shares their journey of migrating from a batch machine learning platform to a real-time event streaming system with Apache Kafka® and delves into their approach to making the transition frictionless. 

Ralph explains that Forecasty.ai was initially built on top of batch processing, however, updating the models with batch-data syncs was costly and environmentally taxing. There was also the question of scalability—progressing from 60 commodities on offer to their eventual plan of over 200 commodities. Ralph observed that most real-time systems are non-batch, streaming-based real-time data platforms with stateful stream processing, using Kafka Streams, Apache Flink®, or even Apache Samza. However, stateful stream processing involves resources, such as teams of stream processing specialists to solve the task. 

With the existing team, Ralph decided to build a real-time data platform without using any sort of stateful stream processing. They strictly keep to the out-of-the-box components, such as Kafka topics, Kafka Producer API, Kafka Consumer API, and other Kafka connectors, along with a real-time database to process data streams and implement the necessary joins inside the database. 

Additionally, Ralph shares the tool he built to handle historical data, kash.py—a Kafka shell based on Python; discusses issues the platform needed to overcome for success, and how they can make the migration from batch processing to stream processing painless for the data science team. 

EPISODE LINKS

• Kafka Streams 101 course
• The Difference Engine for Unlocking the Kafka Black Box
• GitHub repo: kash.py
• Watch the video version of this podcast
• Kris Jenkins’ Twitter
• Streaming Audio Playlist 
• Join the Confluent Community
• Learn more with Kafka tutorials, resources, and guides at Confluent Developer
• Live demo: Intro to Event-Driven Microservices with Confluent
• Use PODCAST100 to get an addi

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.