Confluent Blog

Learn how modern data management approaches like data mesh and event-driven architecture (EDA) can be used to manage data platforms and how to take advantage of them.

When Jade Bowen joined Confluent as an account executive for the enterprise market and 11th employee in the ANZ region, there was only one client for her to work with. Three years later, she’s heading up the entire APAC Customer Success team.

Perhaps the largest challenge for modern data teams is gaining and retaining trust. The challenge of Big Data has come and gone, now we face the challenge of Untrustworthy Data, which will be one of the core focal points of the data space in 2023 and beyond.

Get an introduction to why Python is becoming a popular language for developing Apache Kafka client applications. You will learn about several benefits that Kafka developers gain by using the Python language.

Three years in, Marcus Greer is still excited about the work he does at Confluent. As a software engineer in the Cloud Manageability organization, Marcus helps make customers’ lives easier – giving them insight into the complex systems their businesses depend on.

Discover tools, practices, and patterns for planning geo-replicated Apache Kafka deployments to build reliable, scalable, secure, and globally distributed data pipelines that meet your business needs.

An Approach to combining Change Data Capture (CDC) messages from a relational database into transactional messages using Kafka Streams.

This post details how to minimize internal messaging within Confluent platform clusters. Service mesh and containerized applications have popularized the idea of control and data planes. This post applies it to the Confluent platform clusters and highlights its use in Confluent Cloud.

Using Apache Kafka to decouple microservices is a successful way to build a more resilient, flexible, and scalable architecture. However, it is very common for such microservices to pair with a database. This blog provides a real-world use case on how Kafka replaces a database with ksqlDB.

This article summarizes dynamic versus static consumer group membership in Apache Kafka. It shows how the approaches affect rebalancing in heavy state applications and teaches the user how to choose between the methods.

Who isn’t familiar with Michelin? Whether it’s their extensive product line of tires for nearly every vehicle imaginable (including space shuttles), or the world-renowned Michelin Guide that has determined the standard of excellence for fine dining for over 100 years, you’ve probably heard of them.

Learn how to avoid confusion by implementing co-partitioning.

Learn what windowing is in Kafka Streams and get comfortable with the differences between the main types.

Apache Kafka 3.4 includes early access to ZooKeeper to KRaft migrations, enabling existing Kafka clusters to migrate to KRaft mode and gain scalability and resiliency benefits. Additionally, 3.4 includes several updates to Kafka Core, Streams, Connect, and more.