Displaying 1 to 8 from 8 results

jstorm - Enterprise Stream Process Engine


Alibaba JStorm is an enterprise fast and stable streaming process engine. It runs program up to 4x faster than Apache Storm. It is easy to switch from record mode to mini-batch mode. It is not only a streaming process engine. It means one solution for real time requirement, whole realtime ecosystem.

Apache Storm - Distributed and fault-tolerant realtime computation


Storm is a distributed real time computation system. Storm makes it easy to reliably process unbounded streams of data, doing for real time processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more.

Apache Beam - Unified model for defining both batch and streaming data-parallel processing pipelines


Apache Beam is an open source, unified model for defining both batch and streaming data-parallel processing pipelines. Using one of the open source Beam SDKs, you build a program that defines the pipeline. The pipeline is then executed by one of Beam’s supported distributed processing back-ends, which include Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow.

Hazelcast Jet - Distributed data processing engine, built on top of Hazelcast


Hazelcast Jet is a distributed computing platform built for high-performance stream processing and fast batch processing. It embeds Hazelcast In Memory Data Grid (IMDG) to provide a lightweight package of a processor and a scalable in-memory storage. It supports distributed java.util.stream API support for Hazelcast data structures such as IMap and IList, Distributed implementations of java.util.{Queue, Set, List, Map} data structures highly optimized to be used for the processing




Time-series Framework


Core framework used to manage, process and respond to dynamic changes in fast moving streaming time-series data in real-time.

pluck - Pluck text in a fast and intuitive way :rooster:


In pluck, X and Y are called activators and Z is called the deactivator. The file/URL being plucked is streamed byte-by-byte into a finite state machine. Once all activators are found, the following bytes are saved to a buffer, which is added to a list of results once the deactivator is found.The file is read only once, and multiple queries are extracted simultaneously. Alos, there is no requirement on the file format (e.g. XML/HTML), as long as its text.

sp - Stream Processors on Kafka in Golang


A Swiss army knife for kafka + golang stream processing.