This course introduces the Apache Spark distributed computing engine. It is suitable for developers, data analysts, architects, technical managers, and anyone who needs to use Spark in a hands-on manner. The course is based on the Spark 3.x release, but can be tailored for Spark 2 as well. All examples and labs use Python for programming.
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. DataFrames and Spark SQL). It includes in-depth coverage of Spark SQL and DataFrames, 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.
Students will work through many labs, using Spark through the pyspark 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.