Apache Kafka

Confluent’s Schema IDs in headers transform Kafka from "dumb pipes" to a "smart data plane." By moving metadata out of payloads, teams can schematize topics without breaking legacy apps or requiring big-bang migrations. This unlocks governed, AI-ready data for Flink and lakehouses with ease.

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...

Explore the latest Confluent client updates, featuring Python asyncio general availability, improved support for Schema Registry and more.

Apache Kafka 4.2.0 is here. Explore production-ready share groups, Kafka Streams rebalance GA, new metrics, security enhancements, and upgrade details.

A global investment bank and Confluent used Apache Kafka to deliver sub-5ms p99 end-to-end latency with strict durability. Through disciplined architecture, monitoring, and tuning, they scaled from 100k to 1.6M msgs/s (<5KB), preserving order and transparent tail latency.

Learn how to build a custom Kafka connector, which is an essential skill for anyone working with Apache Kafka® in real-time data streaming environments with a wide variety of data sources and sinks.

Manual schema management in Apache Kafka® leads to rising costs, compatibility risks, and engineering overhead. See how Confluent lowers your total cost of ownership for Kafka with Schema Registry and more.

Apache Kafka® cluster rebalancing seems routine, but it drives hidden costs in time, resources, and cloud spend. Learn how Confluent helps reduce your Kafka total cost of ownership.

Manual Apache Kafka® monitoring and tool sprawl drive hidden costs in time, complexity, and cloud spend. Learn how Confluent lowers total cost of ownership for Kafka with integrated monitoring.

Explore the latest Confluent client updates, featuring KIP-848 general availability for improved consumer stability and native Asyncio support for Python. We’ve also added simplified OAuth metadata authentication for cloud security and new observability metrics for Node.js consumers.

This blog introduces the concept of API chaining — a method where data is collected by sequentially calling multiple related APIs. The response from one API is used to construct the request for the next, creating a chain that enables richer, more contextual data collection.

Planning an Apache Kafka® migration? Learn how to estimate migration expenses, reduce costs, and compare self-managed vs managed real-time data platforms with expert insight.

Explore the hidden costs of real-time streaming—compare infrastructure, ops, and ROI between Confluent Cloud and self-managed Apache Kafka®. Learn how auto-scaling and governance lower TCO.

Confluent, powered by Kafka, is the real-time backbone for agentic systems built with Google Cloud. It enables agents to access fresh data (MCP) and communicate seamlessly (A2A) via a decoupled architecture. This ensures scalability, resilience, and observability for complex, intelligent workflows.