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The first version of Apache Spark was developed by researchers in University of California to help individuals as well as organizations to better manage and stream their data. The programing platform was later acquired by Apache Foundation, and has ever since then being in its care.
When fast computation and bolting data streaming is needed, Apache Spark is your best bet. Designed to run on Hadoop, Apache Spark enables users to quickly process data and perform cluster computation.
It should be noted that Apache Spark differs from Hadoop in more ways than one. Apache Spark manages its own set of clusters differently as does Hadoop. The only purpose for integrating Hadoop in Apache Spark is for efficient data storage.
Lightening Processing Speed: Apache Spark has an impressive computational speed, beating most conventional programs to the game. What other programing platforms can do in 1 minute, Spark can do it in fractions of a second. Its computational speed is a lot faster when working from memory.
Robustness: Spark is robust enough to run machine learning, complex computational algorithm, data streaming and SQL queries.
Super Flexible: Whether you prefer to program in Java, Python or Scala, Spark will meet your needs. This is because it has been designed to support several programing language.
Fast and efficient streaming: One of the uniqueness of Spark is its ability to perform streaming analytics. For efficiency, streaming is done in batches.
Expansive Library: Nothing makes the life of a programmer easy than working with a programing platform with an expansive library. Spark is composed of Mlib (Machine Learning Library) – library capable of handling any computational problem.
Graphx: Graphx is the component that enables Spark handle graphical computation. Graphx is flexible enough to allow users customize their graphs as much as they please.
Spark SQL: This component provides the platform for Spark to handle both structured and partially structured data, quickly and efficiently.
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