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.

Consumers expect immediate, personalized gratification. Real-time distributed stream processing enables companies to meet those expectations. However, many see the technology as being out of reach for ...

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 ...

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.

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 ...

What Is the History of Value Stream Management? Value stream management has a long history dating back to the early 20th century. Rooted in lean management philosophies, it was first introduced in the ...

The process of creating a value stream map takes all the necessary people, processes, information and inventory, and displays them in a flowchart format. By visualizing all elements that go into ...

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.