Data stream processing is defined as a system performing transformations for creating analytics on data inside a stream. In Part 1 of this series, we defined data streaming to provide an understanding ...

Traditionally, obtaining data from a source system and getting it to a target was a batch-based task, but the limitations are clear and event stream processing is the future.

Apache Kafka continues its ascent as attention shifts from lumbering Hadoop and data lakes to real-time streams ...

Confluent Inc. today announced new features in its cloud service that make it easier for users of its Apache Kafka-based streaming engine to store data in the Apache Iceberg table format. The new ...

Apache Kafka is a strong choice to handle real-time data streaming as it ingests, persists and presents streams of data for consumption and use by individuals for analytics. Basically, Kafka operates ...

Straight-talk from Confluent leaders on tackling cloud-native Kafka challenges, driving generative AI with data streaming, and revolutionizing real-time insights.

Learn More Aiven, a cloud-data platform based in Helsinki, has fleshed out an open-source ecosystem for Apache Kafka, a popular event-streaming platform.

Because Kafka is associated with data in motion, it is often confused with streaming engines. But Kafka acts as traffic cop, serving as ingest point from a stream, or transmission point to a stream.

The release of Kafka 0.9, its commercial counterpart Confluent Platform 2.0, and new distro component MapR Streams, bring prominence and maturity to streaming data processing in the Hadoop ecosystem.

The Big Data streaming project Apache Kafka is all over the news lately, highlighted by Confluent Inc.'s new update of its Kafka-based Confluent Platform 2.0.