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Confluent announces the General Availability of Queues for Kafka on Confluent Cloud and Confluent Platform with Apache Kafka 4.2. This production-ready feature brings native queue semantics to Kafka through KIP-932, enabling organizations to consolidate streaming and queuing infrastructure while...
Confluent's AI developer tools are now GA: an open-source local MCP server, a managed MCP server, and Agent Skills. Together they give AI coding assistants direct access to your streaming platform — the tools to act on it and the domain knowledge to build correctly.
Explore new Confluent Intelligence features: enhanced querying with Real-Time Context Engine, PII detection, sentiment analysis, and support for TimesFM, Anthropic, and Fireworks AI models.
The latest ksqlDB release introduces long-awaited features such as tunable retention and grace period for windowed aggregates, new built-in functions including LATEST_BY_OFFSET, a peek at the new server API under […]
Event stream processing solves many business challenges, from big data ingestion and data integration, to real-time data processing and IoT. It gives you the ability to analyze big data streams […]
The world is changing fast, and keeping up can be hard. Companies must evolve their IT to stay modern, providing services that are more and more sophisticated to their customers. […]
We are pleased to announce the release of ksqlDB 0.7.0. This release features highly available state, security enhancements for queries, a broadened range of language/data expressions, performance improvements, bug fixes, […]
We talked about how easy it is to send osquery logs to the Confluent Platform in part 1. Now, we’ll consume streams of osquery logs, detect anomalous behavior using machine […]
Apache Kafka® is often deployed alongside Elasticsearch to perform log exploration, metrics monitoring and alerting, data visualisation, and analytics. It is complementary to Elasticsearch but also overlaps in some ways, […]
When a company becomes overreliant on a centralized database, a world of bad things start to happen. Queries become slow, taxing an overburdened execution engine. Engineering decisions come to a […]
Now that we’ve learned about the processing layer of Apache Kafka® by looking at streams and tables, as well as the architecture of distributed processing with the Kafka Streams API […]
Part 2 of this series discussed in detail the storage layer of Apache Kafka: topics, partitions, and brokers, along with storage formats and event partitioning. Now that we have this […]
Part 1 of this series discussed the basic elements of an event streaming platform: events, streams, and tables. We also introduced the stream-table duality and learned why it is a […]
This four-part series explores the core fundamentals of Kafka’s storage and processing layers and how they interrelate. In this first part, we begin with an overview of events, streams, tables, […]
When KSQL was released, my first blog post about it showed how to use KSQL with Twitter data. Two years later, its successor ksqlDB was born, which we announced this […]
As a test class that allows you to test Kafka Streams logic, TopologyTestDriver is a lot faster than utilizing EmbeddedSingleNodeKafkaCluster and makes it possible to simulate different timing scenarios. Not […]
ksqlDB is a new kind of database purpose-built for stream processing apps, allowing users to build stream processing applications against data in Apache Kafka® and enhancing developer productivity. ksqlDB simplifies […]