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Big data processing tasks can be performed in parallel on a cluster of computers using Apache Spark, an open-source framework. In terms of distributed processing, it is one of the most popular frameworks in the world.

An open source, distributed processing system called Apache Spark is used for large-scale datasets. Data of any size can be quickly analyzed using in-memory caching and optimized query execution. Code can be reused across multiple workloads—batch processing (queries), real-time analytics (machine learning), and graph processing—using the same APIs. There are a wide range of businesses using it, including FINRA, Yelp, Zillow, DataXu, Urban Institute, and CrowdStrike, among others. In 2017, Apache Spark had 365,000 meetup members, making it one of the most popular big data distributed processing frameworks.

One of the most important terms in the field of data management is "Data Management." New approaches and methods are being investigated to create a modern Big Data practice that provides the strength and consistency needed to raise business to a higher level.