Open-Source Big Data Analytics
Definition - What does Open-Source Big Data Analytics mean?
Open-source big data analytics refers to the use of open-source software and tools for analyzing huge quantities of data in order to gather relevant and actionable information that an organization can use in order to further its business goals. The biggest player in open-source big data analytics is Apache's Hadoop – it is the most widely used software library for processing enormous data sets across a cluster of computers using a distributed process for parallelism.
Techopedia explains Open-Source Big Data Analytics
Open-source big data analytics makes use of open-source software and tools in order to execute big data analytics by either using an entire software platform or various open-source tools for different tasks in the process of data analytics. Apache Hadoop is the most well-known system for big data analytics, but other components are required before a real analytics system can be put together.
Hadoop is the open-source implementation of the MapReduce algorithm pioneered by Google and Yahoo, so it is the basis of most analytics systems today. Many big data analytics tools make use of open source, including robust database systems such as the open-source MongoDB, a sophisticated and scalable NoSQL database very suited for big data applications, as well as others.
Open-source big data analytics services encompass:
- Data collection system
- Control center for administering and monitoring clusters
- Machine learning and data mining library
- Application coordination service
- Compute engine
- Execution framework
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