Solutions Review has compiled this list of the best open-source data streaming software and tools to consider right now.
Searching for commercial data streaming software can be a daunting (and expensive) process, one that requires long hours of research and deep pockets. The most popular enterprise data streaming tools often provide more than what’s necessary for smaller organizations, with advanced functionality relevant to only the most technically savvy users. Thankfully, there are a number of viable open-source data streaming tooling out there.
In this article, we will examine the best open-source data streaming software and tools, first by providing a brief overview of what to expect and also with short blurbs about each of the currently available options in the space. This is the most complete and up-to-date directory on the web.
Note: The best open-source data streaming software and tools are listed in alphabetical order.
The Best Open-Source Data Streaming Software and Tools
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, and perform computations at in-memory speed and at any scale. Precise control of time and state enables Flink’s runtime to run any kind of application on unbounded streams. Bounded streams are internally processed by algorithms and data structures that are specifically designed for fixed-sized data sets.
Apache Kafka is a distributed streaming platform that enables users to publish and subscribe to streams of records, store streams of records, and process them as they occur. Kafka is most notably used for building real-time streaming data pipelines and applications and is run as a cluster on one or more servers that can span more than one datacenter. The Kafka cluster stores streams of records in categories called topics, and each record consists of a key, a value, and a timestamp.
Apache Spark is a unified analytics engine for large-scale data processing. It is noted for its high performance for both batch and streaming data by using a DAG scheduler, query optimizer, and a physical execution engine. Spark offers more than 80 high-level operators that can be used interactively from the Scala, Python, R, and SQL shells. The engine powers a stack of libraries including SQL and DataFrames, MLib for machine learning, GraphX, and Spark Streaming. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud.
Apache Storm is a free and open-source distributed real-time computation system. Apache Storm makes it easy to reliably process unbounded streams of data, doing for real-time processing what Hadoop did for batch processing. Apache Storm is simple and can be used with any programming language. An Apache Storm topology consumes streams of data and processes those streams in arbitrarily complex ways, repartitioning the streams between each stage of the computation however needed. Read more in the tutorial.
Apache Samza allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka. It supports flexible deployment options to run on YARN or as a standalone library. Samza provides extremely low latencies and high throughput for analyzing data, scales to several terabytes of state with features like incremental checkpoints, touts API connectors for building applications, and the ability to run the same code to process both batch and streaming data.
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