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.

A data streaming solution must account for speed, security, scaling and more if it’s to provide the productivity, service and information it’s designed to deliver.

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 ...

Confluent has launched Streaming Agents to embed AI directly into data streams, addressing the enterprise challenge of moving ...

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 ...

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.

The streaming analytics market is expected to record strong growth in the 2025 to 2035 period with increased usage of ...

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 ...

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 ...