Apache Spark has become one of the key
cluster-computing frame works in the world. Spark can be deployed in numereous
ways like in machine Learning, Streaming data and graphic processing. Spark
supports programming languages like Python, Scala, Java, and R.
Apache Hadoop is an open-source framework written in
Java that allows us to store and process Big Data in a distributed environment,
across various clusters of computers using simple programming constructs. To do
this, Hadoop uses an algorithm called Map Reduce, which divides the task into small parts and
assigns them to a set of computers. Hadoop also has its own file system, Hadoop
Distributed File System (HDFS), which is based on Google File
System (GFS). HDFS is designed to run on low-cost hardware.
Apache Spark is an open-source distributed
cluster-computing framework. Spark is a data processing engine developed to provide
faster and easy-to-use analytics than Hadoop Ma pReduce.
Spark in the big data industry is because of its in-memory data processing that
makes it high-speed data processing engine compare to Map Reduce. Apache Spark
has huge potential to contribute to Big data related business in the
Apache Spark is
a Big data processing interface which provides not only programming interface
in the data cluster but also adequate fault tolerance and data parallelism.
This open-source platform is efficient in speedy processing of massive
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