Stream processing, through open source technology frameworks like Apache Flink, is one of the best-suited approaches for the modern enterprise to take the leap forward and become real time. Stream ...

Hybrid processing combines both methods and reaps the benefits of both. In general, the data is processed as a stream and simultaneously branched off to storage for later batch processing.

Stream processors are well suited for traditional GPU-focused tasks such as image, video and signal processing. It is becoming increasingly common, however, to also utilize the stream processors ...

Decodable, the well-funded real-time data engineering and stream processing platform based, in part, on the Apache Flink open source project, is launching a major update today that aims to make ...

2. Scalability and performance Transactional stream processing systems are designed for scalability. They can handle large volumes of data with ease, thanks to their distributed nature.

Stream processing is an alternative to batch processing. Most users want to do something with the data at the time it is created, rather than wait for some predetermined interval.