Apache Spark (http://spark.apache.org/) is an open source, fast cluster-computing platform. It was originally created by AMPLab at the University of California, Berkeley. Its source code was later donated to the Apache Software Foundation (https://www.apache.org/). Spark comes with a very fast computation speed because data is loaded into distributed memory (RAM) across a cluster of machines. Not only can data be quickly transformed, but also cached on demand for a variety of use cases. Compared to Hadoop MapReduce, it runs programs up to 100 times faster when the data fits in memory, or 10 times faster on disk. Spark provides support for four programming languages: Java, Scala, Python, and R. This book covers the Spark APIs (and deep learning frameworks) for Scala (https://www.scala-lang.org/) and Python (https://www.python.org/) only.
This chapter will cover the following topics:
- Apache Spark fundamentals
- Getting Spark
- Resilient Distributed Dataset (RDD) programming
- Spark SQL, Datasets, and DataFrames
- Spark Streaming
- Cluster mode using a different manager
