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

Focused on optimizing Apache Kafka® performance with maximized efficiency, Confluent’s Product Infrastructure team has been actively exploring opportunities for scaling out Kafka clusters. They are able to run Kafka workloads with half the typical memory usage while saving infrastructure costs, which they have tested and now safely rolled out across Confluent Cloud. 

After spending seven years at Amazon Web Services (AWS) working on search services and Amazon Aurora as a software engineer, Adithya Chandra decided to apply his expertise in cluster management, load balancing, elasticity, and performance of search and storage clusters to the Confluent team.

Last year, Confluent shipped Tiered Storage, which moves eligible data to remote storage from a Kafka broker. As most of the data moves to remote storage, we can upgrade to better storage volumes backed by solid-state drives (SSDs). SSDs are capable of higher throughput compared to hard disk drives (HDDs), capable of fast, random IO, yet more expensive per provisioned gigabyte. Given that SSDs are useful at random IO and can support higher throughput, Confluent started investigating whether it was possible to run Kafka with lesser RAM, which is comparatively much more expensive per gigabyte compared to SSD. Instance types in the cloud had the same CPU but half the memory was 20% cheaper.

In this episode, Adithya covers how to run Kafka more efficiently on Confluent Cloud and dives into the following:

• Memory allocation on an instance running Kafka
• What is a JVM heap? Why should it be sized? How much is enough? What are the downsides of a small heap?
• Memory usage of Datadog, Kubernetes, and other processes, and allocating memory correctly
• What is the ideal page cache size? What is a page cache used for? Are there any parameters that can be tuned? How does Kafka use the page cache?
• Testing via the simulation of a variety of workloads using Trogdor
• High-throughput, high-connection, and high-partition tests and their results
• Available cloud hardware and finding the best fit, including choosing the number of instance types, migrating from one instance to another, and using nodepools to migrate brokers safely, one by one
• What do you do when your preferred hardware is not available? Can you run hybrid Kafka clusters if the preferred instance is not widely available?
• Building infrastructure that allows you to perform testing easily and that can support newer hardware faster (ARM processors, SSDs, etc.)

EPISODE LINKS

• 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.

When compiling database reports using a variety of data from different systems, obtaining the right data when you need it in real time can be difficult. With cloud connectivity and distributed data pipelines, Pat Helland (Principal Architect, Salesforce) explains how to make educated partial answers when you need to use the Apache Kafka® platform. After all, you can’t get guarantees across a distance, making it critical to consider partial results.

Despite best efforts, managing systems from a distance can result in lag time. The secret, according to Helland, is to anticipate these situations and have a plan for when (not if) they happen. Your outputs may be incomplete from time to time, but that doesn’t mean that there isn’t valuable information and data to be shared. Although you cannot guarantee that stream data will be available when you need it, you can gather replicas within a batch to obtain a consistent result, also known as convergence. Distributed systems of all sizes and across large distances rely on reference architecture for database reporting. 

Plan and anticipate that there will be incomplete inputs at times. Regardless of the types of data that you’re using within a distributed database, there are many inferences that can be made from repetitive monitoring over time. There would be no reason to throw out data from 19 machines when you’re only waiting on one while approaching a deadline. You can make the sources that you have work by making the most out of what is available in the presence of a partition for the overall distributed database.

Confluent Cloud and convergence capabilities have allowed Salesforce to make decisions very quickly even when only partial data is available using replicated systems across multiple databases. This analytical approach is vital for consistency for large enterprises, especially those that depend on multi-cloud functionality. 

EPISODE LINKS

• 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.

Jason Gustafson and Colin McCabe, Apache Kafka® developers, discuss the project to remove ZooKeeper—now known as the KRaft (Kafka on Raft) project. A previous episode of Streaming Audio featured both developers on the podcast before the release of Apache Kafka 2.8. Now they’re back to share their progress.

The KRraft code has been merged (and continues to be merged) in phases. Both developers talk about the foundational Kafka Improvement Proposals (KIPs), such as KIP-595: a Raft protocol for Kafka, and KIP-631: the quorum-based Kafka controller. The idea going into this new release was to give users a chance to try out no-ZooKeeper mode for themselves. 

There are a lot of exciting milestones on the way for KRaft. The next release will feature Raft snapshot support, as well as support for running with security authorizers enabled.  

EPISODE LINKS

• KIP-500: Apache Kafka Without ZooKeeper ft. Colin McCabe and Jason Gustafson
• What’s New in Apache Kafka 2.8
• 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.

What is the internet of things (IoT), and how does it relate to event streaming and Apache Kafka®? The deployment of Kafka outside the datacenter creates many new possibilities for processing data in motion and building new business cases.

In this episode, Kai Waehner, field CTO and global technology advisor at Confluent, discusses the intersection of edge data infrastructure, IoT, and cloud services for Kafka. He also details how businesses get into the sticky situation of not accounting for solutions when data is running dangerously close to the edge. Air-gapped environments and strong security requirements are the norm in many edge deployments.

Defining the edge for your industry depends on what sector you’re in plus the amount of data and interaction involved with your customers. The edge could lie on various points of the spectrum and carry various meanings to various people. Before you can deploy Kafka to the edge, you must first define where that edge is as it relates to your connectivity needs. 

Edge resiliency enables your enterprise to not only control your datacenter with ease but also preserve the data without privacy risks or data leaks. If a business does not have the personnel to handle these big IT jobs on their own or an organization simply does not have an IT department at all, this is where Kafka solutions can come in to fill the gap. 

This podcast explores use cases and architectures at the edge (i.e., outside the datacenter) across industries, including manufacturing, energy, retail, restaurants, and banks. The trade-offs of edge deployments are compared to a hybrid integration with Confluent Cloud. 

EPISODE LINKS

• Processing IoT Data End to End with MQTT & Kafka
• End-to-End Integration: IoT Edge to Confluent
• Infrastructure Checklist for Kafka at the Edge
• Use Cases & Architectures for Kafka at the Edge
• Architecture Patterns for Distributed, Hybrid, Edge & Global Kafka Deployments
• Building a Smart Factory with Kafka & 5G Campus Networks
• Kafka Is the New Black at the Edge in Industrial IoT, Logistics & Retailing 
• Kafka, KSQL & Apache PLC4X for IIoT Data Integration & Processing 
• Streaming Machine Learning at Scale from 100K IoT Devices with HiveMQ, Kafka & TensorFlow 
• Watch this podcast on YouTube
• Join the Confluent Community
• 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.

Imagine if you could create a better world for future generations simply by delivering marine ingenuity. 

Van Oord is a Dutch family-owned company that has served as an international marine contractor for over 150 years, focusing on dredging, land infrastructure in the Netherlands, and offshore wind and oil & gas infrastructure.

Real-time insights into costs spent, the progress of projects, and the performance tracking of vessels and equipment are essential for surviving as a business. Becoming a data-driven company requires that all data connected, synchronized, and visualized—in fact, truly digitized.

This requires a central nervous system that supports:

• Legacy (monolith environment) as well as microservices
• ELT/ETL/streaming ETL
• All types of data, including transactional, streaming, geo, machine, and (sea) survey/bathymetry
• Master data/enterprise common data model

The need for agility and speed makes it necessary to have a fully integrated DevOps-infrastructure-as-code environment, where data lineage, data governance, and enterprise architecture are holistically embedded. Thousands of topics need to be developed, updated, tested, accepted, and deployed each day. This together with different scripts for connectors requires a holistic data management solution, where data lineage, data governance and enterprise architecture are an integrated part.

Thus, Marlon Hiralal (Enterprise/Data Management Architect, Van Oord) and Andreas Wombacher (Data Engineer, Van Oord) turned to Confluent for a three-month proof of concept and explored the pre-prep stage of using Apache Kafka® on Van Oord’s vessels.

Since the environment in Van Oord is dynamic with regards to the application landscape and offered services, it is essential that a stable environment with controlled continuous integration and deployment is applied. Beyond the software components itself, this also applies to configurations and infrastructure, as well as applying the concept of CI/CD with infrastructure as code. The result: using Terraform and Confluent together.

Publishing information is treated as a product at Van Oord. An information product is a set of Kafka topics: topics to communicate change (via change data capture) and topics for sharing the state of a data source (Kafka tables). The set of all information products forms the enterprise data model.

Apache Atlas is used as a data dictionary and governance tool to capture the meaning of different information products. All changes in the data dictionary are available as an information product in Confluent, allowing for consumers of information products to subscribe to the information and be notified about changes.

Van Oord’s enterprise architecture model must remain up to date and aligned with the current implementation. This is achieved by automatically inspecting and analyzing Confluent data flows. Fortunately, Confluent embeds homogeneously in this holistic reference architecture. The basis of the holistic reference architecture is a change data capture (CDC) layer and a persistent layer, which makes Confluent the core component of the Van Oord future-proof digital data management solution.

EPISODE LINKS

• 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.

At Klarna, Lead Engineer Tommy Brunn is building a runtime platform for developers. But outside of his professional role, he is also one of the authors of the JavaScript client for Apache Kafka® called KafkaJS, which has grown from being a niche open source project to the most downloaded Kafka client for Node.js since 2018.

Using Kafka in Node.js has previously meant relying on community-contributed bindings to librdkafka, which required you to spend more of your time debugging failed builds than working on your application. With the original authors moving away from supporting the bindings, and the community only partially picking up the slack, using Kafka on NodeJS was a painful proposition.

Kafka is a core part of Klarna’s microservice architecture, with hundreds of services using it to communicate among themselves. In 2017, as their engineering team was building the ecosystem of Node.js services powering the Klarna app, it was clear that the experience of working with any of the available Kafka clients was not good enough, so they decided to perform something similar for the Erlang client, Brod, and build their own. Rather than wrapping librdkafka, their client is a complete reimplementation in native JavaScript, allowing for a far superior user experience at the cost of being a lot more work to implement. Towards the end of 2017, KafkaJS 0.1.0 was released.

Tommy has also used KafkaJS to build several Kafka-powered services at Klarna, as well as worked on supporting libraries such as integrations with Confluent Schema Registry and Zstandard compression.

Since KafkaJS is written entirely in JavaScript, there is no build step required. It will work 100% of the time in any version of Node.js and evolve together with the platform with no effort required from the end user. It also unlocks some creative use cases. For example, Klarna once did an experiment where they got it to run in a browser. KafkaJS will also run on any platform that’s supported by Node.js, such as ARM. Klarna’s “no dependencies” policy also means that the deployment footprint is small, which makes it a perfect fit for serverless environments.

EPISODE LINKS

• Node.js ❤️ Apache Kafka – Getting Started with KafkaJS 
• 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.

Apache Kafka 2.8 is out! This release includes early access to the long-anticipated ZooKeeper removal encapsulated in KIP-500, as well as other key updates, including the addition of a Describe Cluster API, support for mutual TLS authentication on SASL_SSL listeners, exposed task configurations in the Kafka Connect REST API, the removal of a properties argument for the TopologyTestDriver, the introduction of a Kafka Streams specific uncaught exception handler, improved handling of window size in Streams, and more.

EPISODE LINKS

• Read about what’s new in Apache Kafka 2.8
• Check out the Apache Kafka 2.8 release notes
• 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.

When building solutions for customers in Microsoft Azure, it is not uncommon to come across customers who are deeply entrenched in the Apache Kafka® ecosystem and want to continue expanding within it. Thus, figuring out how to connect Azure first-party services to this ecosystem is of the utmost importance.

Ryan CrawCour is a Microsoft engineer who has been working on all things data and analytics for the past 10+ years, including building out services like Azure Cosmos DB, which is used by millions of people around the globe. More recently, Ryan has taken a customer-facing role where he gets to help customers build the best solutions possible using Microsoft Azure’s cloud platform and development tools. 

In one case, Ryan helped a customer leverage their existing Kafka investments and persist event messages in a durable managed database system in Azure. They chose Azure Cosmos DB, a fully managed, distributed, modern NoSQL database service as their preferred database, but the question remained as to how they would feed events from their Kafka infrastructure into Azure Cosmos DB, as well as how they could get changes from their database system back into their Kafka topics. 

Although integration is in his blood, Ryan confesses that he is relatively new to the world of Kafka and has learned to adjust to what he finds in his customers’ environments. Oftentimes this is Kafka, and for many good reasons, customers don’t want to change this core part of their solution infrastructure. This has led him to embrace Kafka and the ecosystem around it, enabling him to better serve customers. 

He’s been closely tracking the development and progress of Kafka Connect. To him, it is the natural step from Kafka as a messaging infrastructure to Kafka as a key pillar in an integration scenario. Kafka Connect can be thought of as a piece of middleware that can be used to connect a variety of systems to Kafka in a bidirectional manner. This means getting data from Kafka into your downstream systems, often databases, and also taking changes that occur in these systems and publishing them back to Kafka where other systems can then react. 

One day, a customer asked him how to connect Azure Cosmos DB to Kafka. There wasn’t a connector at the time, so he helped build two with the Confluent team: a sink connector, where data flows from Kafka topics into Azure Cosmos DB, as well as a source connector, where Azure Cosmos DB is the source of data pushing changes that occur in the database into Kafka topics.

EPISODE LINKS

• Integrating Azure and Confluent: Ingesting Data to Azure Cosmos DB through Apache Kafka 
• Download the Azure Cosmos DB Connector (Source and Sink) 
• Join the Confluent Community
• GitHub: Kafka Connect for Azure Cosmos DB
• Watch the video version of this podcast
• 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.

If you’ve heard the term “clusters,” then you might know it refers to Confluent components and features that we run in all three major cloud providers today, including an event streaming platform based on Apache Kafka®, ksqlDB, Kafka Connect, the Kafka API, databalancers, and Kafka API services. Rashmi Prabhu, a software engineer on the Control Plane team at Confluent, has the opportunity to help govern the data plane that comprises all these clusters and enables API-driven operations on these clusters. 

But running operations on the cloud in a scaling organization can be time consuming, error prone, and tedious. This episode addresses manual upgrades and rolling restarts of Confluent Cloud clusters during releases, fixes, experiments, and the like, and more importantly, the progress that’s been made to switch from manual operations to an almost fully automated process. You’ll get a sneak peek into what upcoming plans to make cluster operations a fully automated process using the Cluster Upgrader, a new microservice in Java built with Vertx. This service runs as part of the control plane and exposes an API to the user to submit their workflows and target a set of clusters. It performs statement management on the workflow in the backend using Postgres.

So what’s next? Looking forward, there will be the selection phase will be improved to support policy-based deployment strategies that enable you to plan ahead and choose how you want to phase your deployments (e.g., first Azure followed by part of Amazon Web Services and then Google Cloud, or maybe Confluent internal clusters on all cloud providers followed by customer clusters on Google Cloud, Azure, and finally AWS)—the possibilities are endless! 

The process will become more flexible, more configurable, and more error tolerant so that you can take measured risks and experience a standardized way of operating Cloud. In addition, expanding operation automations to internal application deployments and other kinds of fleet management operations that fit the “Select/Apply/Monitor” paradigm are in the works.

EPISODE LINKS

• Watch Project Metamorphosis videos 
• Learn about elastic scaling with Apache Kafka
• Nick Carr: The Many Ways Cloud Computing Will Disrupt IT 
• 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.

As most developers and architects know, data always needs to be accessible no matter what happens outside of the system. This week, Tim Berglund virtually sits down with Anna McDonald (Principal Customer Success Technical Architect, Confluent) to discuss how Automatic Observer Promotion (AOP) can help solve the Apache Kafka® 2.5 datacenter dilemma as a feature now available in Confluent Platform 6.1 and above. Many industries must have a backup plan not only to do the right thing by the data that they collect but because they are regulated by law to do so. 

Anna has a knack for preparing operations that makes replication of data possible both synchronously and asynchronously. To avoid roadblocks in stretch clusters, she’s found that you need both a replication factor and a minimum in-sync replica (ISR). There needs to be a consideration for not just one but multiple copies for the protection of your data criteria. Not replicating the correct number on the datacenter can mean that your application is down, and there’s no way to retrieve vital information during this outage. The presence of observers enables asynchronous replicas that don’t count towards that minimum ISR. 

These ISRs work because they help recover data without invalidating any other standards. Architects should try to maintain topic availability during an event in a two-zone configuration. This assures that the writes go to both zones during normal operation without compromise. With the newest version of Confluent, you can get data in sync and within the minimum ISR. AOP is an excellent solution for developers who want to prepare for the unexpected and maintain accessibility across zones. When you can avoid manual interruption, you’re more likely to avoid errors and tedious operations, which would otherwise lead to a higher probability of data loss. 

In other exciting news, Anna shares about discovering patterns in order to make the entire Confluent ecosystem more automatic. 

EPISODE LINKS

• Automatic Observer Promotion Brings Fast and Safe Multi-Datacenter Failover with Confluent Platform 6.1
• Amusing Ourselves to Death
• 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.