Technology

The big data revolution of the early 2000s saw rapid growth in data creation, storage, and processing. A new set of architectures, tools, and technologies emerged to meet the demand. But what of big data today? You seldom hear of it anymore. Where has it gone?

Use the Confluent CLI and API to create Stream Designer pipelines from SQL source code.

Experienced technology leaders know that adopting a new technology can be risky. Often, we are unable to distinguish between those investments that will be transformational and those that won’t be worthwhile. This post examines how one can decide if event streaming makes sense for them.

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.

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.

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.

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.