What is Apache Spark?

 



Apache Spark is an open-source, distributed processing system commonly used for big data workloads. It is a powerful open-source data processing engine built around speed, ease of use, and advanced analytics. It utilizes in-memory caching and optimized query execution to speed up data processing. It was originally developed at UC Berkeley's AMPLab in 2009, and open sourced in 2010.

Spark can be run on Hadoop, Mesos, or in stand-alone mode. It can access data in a variety of formats, including HDFS, Cassandra, HBase, and S3. Spark provides a rich set of features, including support for SQL, DataFrames, machine learning, and streaming. It also offers a robust set of development tools, including an interactive shell and a powerful Java API. Spark is an excellent choice for big data workloads due to the following advantages

- Speed: Spark can process data much faster than Hadoop, due to its in-memory computing capabilities. - Ease of use: Spark's simple API makes it easy to develop and run data processing applications. - Advanced analytics: Spark includes a wide variety of advanced analytics capabilities, including machine learning and graph processing.

Find more information at: https://spark.apache.org/docs/latest/

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