Spark is often considered as a new, faster, and more advanced engine for Big Data analytics that could soon overthrow Hadoop as the most widely used Big Data tool. In fact, there is already a visible trend for many businesses to opt for Spark rather than Hadoop in their daily data processing activities. Undoubtedly, Spark has several selling points that make it a more attractive alternative to the slightly complicated, and sometimes clunky Hadoop:
It's pretty fast and can reduce the processing time by up to 100 times when run in memory as compared to standard Hadoop MapReduce jobs or up to 10 times if run on disk.
It's a very flexible tool that can run as a standalone application, but also can be deployed on top of Hadoop and HDFS, and other distributed file systems.
It can use a variety of data sources from standard relational databases, through HBase, Hive, to Amazon S3 containers. It may also be launched in the cloud. In fact, in the tutorial in this chapter...