Apache Spark is an open-source unified analytics engine for large-scale data processing.
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11 Facts About Apache Spark
2.
Apache Spark provides an interface for programming clusters with implicit data parallelism and fault tolerance.
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3.
Apache Spark has its architectural foundation in the resilient distributed dataset, a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way.
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4.
Apache Spark requires a cluster manager and a distributed storage system.
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5.
Besides the RDD-oriented functional style of programming, Apache Spark provides two restricted forms of shared variables: broadcast variables reference read-only data that needs to be available on all nodes, while accumulators can be used to program reductions in an imperative style.
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6.
Apache Spark SQL is a component on top of Apache Spark Core that introduced a data abstraction called DataFrames, which provides support for structured and semi-structured data.
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8.
Apache Spark Streaming uses Apache Spark Core's fast scheduling capability to perform streaming analytics.
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9.
Apache Spark can be deployed in a traditional on-premises data center as well as in the cloud.
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10.
Apache Spark has built-in support for Scala, Java, R, and Python with 3rd party support for the.
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11.
Apache Spark was initially started by Matei Zaharia at UC Berkeley's AMPLab in 2009, and open sourced in 2010 under a BSD license.
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