Practically every company can win with the processing of streaming data, but it takes a careful shift to this new paradigm of continuous processing of streaming data.

Confluent has unveiled new capabilities that unite batch and stream processing to enable more effective AI applications and agents. The aim? Confluent wants to position itself as an essential platform ...

Stream processing can also be used to power real-time generative AI, and help to build applications that leverage always up-to-date data with the power tools such as ChatGPT as explained here.

Confluent Inc. today announced expanded capabilities for its managed service for Apache Flink, the open-source big data processing framework. Unlike the regular open-source Flink, it comes with a ...

Batch data processing is too slow for real-time AI: How open-source Apache Airflow 3.0 solves the challenge with event-driven data orchestration ...

The race is on to process high volumes of data at a rapid pace to future-proof and stay ahead of competitors. Holger Temme of Confluent makes the case for stream processing, with some industry ...

That’s stream processing. To stream data and not take advantage of its hidden value – by processing it at the same time – is a huge missed opportunity.

Because Apache Flink processes data in real time and can be applied to unbounded datasets, it is quickly emerging as the stream processing engine of choice for streaming data applications.

Speed to market – Accelerate time to value with a complete, ready-to-use data streaming platform including 120+ Kafka connectors, Flink stream processing, enterprise-grade security and data ...