New distributed processing engine for big data streams lands
|Michael Haynes in Big Data Wednesday, February 8, 2017|
Jet is built on top of a one-record-per-time architecture (sometimes known as continuous operators). This means that it processes incoming records as soon as possible, opposed to accumulating records into micro-batches, consequently lowering latency for applications. Jet ingests data at high-velocity (via socket, file, HDFS or Kafka interfaces), and processes the business logic or complex computation on incoming data. A pure in-memory approach, Jet is 20x faster than Hadoop, enabling users to meet service-level requirements. It is extremely simple to program and to deploy - in particular Jet can be fully embedded for OEMs and for Microservices - making it is easier for manufacturers to build and maintain next generation systems.
- Ingest high volumes of data, while keeping up-to-date with what’s coming in;
- Anomaly detection, recognize and validate the correctness of data entering the system;
- Analyze, aggregate, extrapolate and react to a situation in near real-time;
- Work with extremely low latency access to data; and
- Reporting, whether running on-demand or via scheduled batch processing.
Greg Luck, CEO of Hazelcast, said: “Hazelcast Jet is a super fast, low latency, next generation DAG Engine for Big Data processing. We believe that the Hadoop and Spark ecosystems are too complex to program and to deploy and have set out to bring Hazelcast’s legendary simplicity to Big Data. We have designed it as a general purpose engine for the intersect of Big Data programmers and Java programmers. But if you are already a Hazelcast user or have data in Hazelcast it will be the easiest way to solve your Big Data problems. ”
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