Batch processing, a long-established model, involves accumulating data and processing it in periodic batches upon receiving user query requests. Stream processing, on the other hand, continuously ...

Apache Tez framework opens the door to a new generation of high-performance, interactive, distributed data processing applications ...

Materialize today announced early availability of its distributed streaming database, which enables immediate, widespread adoption of real-time data f ...

From the outside, Flink does not look like anything more than just another member of that processing family inside an ever-expanding open source analytics toolchain. It’s in the same family as Hadoop ...

AWS recently announced a distributed map for Step Functions, a solution for large-scale parallel data processing. Optimized for S3, the new feature of the AWS orchestration service targets ...

For batch data processing, MadReduce is an ideal choice, but for many, the non-batch functions, such as real-time data processing and graph processing need more than what classical MapReduce can ...

In batch processing, if costs are not isolated, high-volume customers and products tend to subsidize lower-volume ones. This article reviews different types of batch activities and how they would be ...

Rockwell Automation’s SequenceManager brings batch processing down to the controller, reducing engineering time and infrastructure costs for standalone skid-based systems.

At QCon San Francisco 2016, Neha Narkhede presented “ETL is Dead; Long Live Streams”, and discussed the changing landscape of enterprise data processing.