Technology Blog

Ever dealt with a misbehaving consumer group? Imbalanced broker load? This could be due to your consumer group and partitioning strategy!

Today, we’re introducing Confluent Cloud’s fully managed service for Apache Flink®, improvements to Kora Engine, how AI and streaming work together, and much more.

As of today, Apache Flink® on Confluent Cloud is available for preview in select regions on AWS. In this post, learn how we’ve re-architected Flink as a cloud-native service on Confluent Cloud.

Learn the best practices for integrating Confluent with AWS Lambda to build event-driven architectures.

Learn about the role of batch.size and linger.ms in data compression.

Learn how to build a Java pipeline that consumes clickstream data from Apache Kafka®. Consuming clickstreams is something that many businesses have a use for and it can also be generalized to consuming other types of streaming data.

Dive into Flink SQL, a powerful data processing engine that allows you to process and analyze large volumes of data in real time. We’ll cover how Flink SQL relates to the other Flink APIs and showcase some of its built-in functions and operations with syntax examples.

Is Windows your favorite development environment? Do you want to run Apache Kafka® on Windows? Thanks to the Windows Subsystem for Linux 2 (WSL 2), now you can, and with fewer tears than in the past.

Apache Kafka (the basis for the Confluent Platform) delivers an advanced stream processing platform for streaming data across AWS, GCP, and Azure at scale, used by thousands of companies. Amazon...

Get a high-level overview of source connector tuning: What can and cannot be tuned, and tuning methodology for any and all source connectors.

Learn about Confluent Platform 7.5 and its latest key features: enhancing security with SSO for Control Center, improving developer efficacy with Confluent REST Proxy API v3, and improving disaster recovery capabilities with bidirectional Cluster Linking.

Apache Flink can be used for multiple stream processing use cases. In this post we show how developers can use Flink to build real-time applications, run analytical workloads or build real-time pipelines.

Versioned key-value state stores, introduced to Kafka Streams in 3.5, enhance stateful processing capabilities by allowing users to store multiple record versions per key, rather than only the single latest version per key as is the case for existing key-value stores today...

Learn why stream processing is such a critical component of the data streaming stack, why developers are choosing Apache Flink as their stream processing framework of choice, and how to use Flink with Kafka.