Stream processing systems are pivotal to modern data-driven environments, enabling the continual ingestion, processing and analysis of unbounded data streams across distributed computing resources.

Stream processing unifies applications and analytics by processing data as it arrives, in real-time, and detects conditions within a short period of time from when data is received. The key strength ...

We live in a world in motion. Stream processing allows us to record events in the real world so that we can take action or make predictions that will drive better business outcomes. The real world is ...

Streaming data records are typically small, measured in mere kilobytes, but the stream often goes on and on without ever stopping. Streaming data, also called event stream processing, is usually ...

On Confluent Cloud for Apache Flink®, snapshot queries combine batch and stream processing to enable AI apps and agents to act on past and present data New private networking and security features ...

Value stream map vs. process map, which is better for you? As with many tools and methods in Six Sigma, there isn’t a one-size-fits-all approach to mapping things out. Both of these tools have ...

Confluent CEO Jay Kreps argues that data stored in warehouses or lakehouses aren’t appropriate for the reliable and well-governed AI agents. Confluent CEO Jay Kreps took to the stage at the vendor’s ...