This course introduces the Apache Spark distributed computing engine, and is suitable for developers, data analysts, architects, technical managers, and anyone who needs to use Spark in a hands-on manner. It is based on the Spark 3.x release.
The course provides a solid technical introduction to the Spark architecture and how Spark works. It covers the basic building blocks of Spark (e.g. RDDs and the distributed compute engine), as well as higher-level constructs that provide a simpler and more capable interface (e.g. DataSets/DataFrames and Spark SQL). It includes in-depth coverage of Spark SQL, DataFrames, and DataSets, which are now the preferred programming API. This includes exploring possible performance issues and strategies for optimization.
The course also covers more advanced capabilities such as the use of Spark Streaming to process streaming data, and integrating with the Kafka server.
The course is very hands-on, with many labs. Participants will interact with Spark through the Spark shell (for interactive, ad-hoc processing) as well as through programs using the Spark API. After taking this course, you will be ready to work with Spark in an informed and productive manner.
Labs are supported in Scala. There is a separate course for Python users.