In-Person Training
Apache Spark programming

Brooke Wenig walks you through the core APIs for using Spark, fundamental mechanisms and basic internals of the framework, SQL and other high-level data access tools, and Spark’s streaming capabilities and machine learning APIs. Join in to learn how to perform machine learning on Spark and explore the algorithms supported by the Spark MLlib APIs.

Each topic includes lecture content along with hands-on use of Spark through an elegant web-based notebook environment. Notebooks allow attendees to code jobs, data analysis queries, and visualizations using their own Spark cluster, accessed through a web browser. You can keep the notebooks and continue to use them with the free Databricks Community Edition offering. Alternatively, each notebook can be exported as source code and run within any Spark environment.

Outline

Spark overview

• The DataFrames programming API
• Spark SQL
• The Catalyst query optimizer
• The Tungsten in-memory data format
• The Dataset API, encoders, and decoders
• Use of the Spark UI to help understand DataFrame behavior and performance
• Caching and storage levels

Spark internals

• How Spark schedules and executes jobs and tasks
• Shuffling, shuffle files, and performance
• How various data sources are partitioned
• How Spark handles data reads and writes

Graph processing with GraphFrames

Spark ML’s Pipeline API for machine learning

Spark Structured Streaming