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 2.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. 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.
Before taking this course, students should be familiar with programming principles and have previous experience in software development using Scala. Previous experience with data streaming, SQL, and HDP is also helpful, but not required.
4 Days/Lecture & Lab
This course is designed for software engineers that are looking to develop in-memory applications for time sensitive and highly iterative applications in an Enterprise HDP environment.
- Scala Ramp Up, Introduction to Spark
- RDDs and Spark Architecture, Spark SQL, DataFrames and DataSets
- Shuffling, Transformations and Performance, Performance Tuning
- Creating Standalone Applications and Spark Streaming