Apache Spark is a unified engine that is based on a parallel cluster-based computing model extending the Hadoop MapReduce model. It developed as an open-source project from the research of a Ph.D. student at Berkeley. Apache Spark can handle various parallel and different workloads to process big data.
Spark's foundation layers are low-level granular APIs and structured APIs. The low-level APIs work on RDDs and distributed variables, while the structured APIs work on datasets, DataFrames, and support querying using SQL. Spark's streaming functionalities, unified advanced analytics, and the ecosystem of numerous libraries are built on the foundations of low-level and structured APIs:
Apache Spark emerged as an attempt to bridge and close some of the gaps in Hadoop/MapReduce. While MapReduce has had its early adoption and traction as a general batch processing engine in companies such as Facebook and Google...