Apache Spark with Python Foundation is a course that 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. 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.
The course is very hands-on, with many labs. Participants will interact with 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.
Who Should Attend
Application developers, analysts and data scientists
What Attendees Will Learn
- Understand the need for Spark in data processing
- Understand the Spark architecture and how it distributes computations to cluster nodes
- Be familiar with basic installation / setup / layout of Spark
- Use the Spark shell for interactive and ad-hoc operations
- Understand RDDs (Resilient Distributed Datasets), and data partitioning, pipelining, and computations
- Understand/use RDD ops such as map(), filter() and others.
- Understand and use Spark SQL and the DataFrame API.
- Understand the DataFrame capabilities, including the Catalyst query optimizer and Tungsten memory/cpu optimizations.
- Be familiar with performance issues, and use DataFrames and Spark SQL for efficient computations
- Understand Spark’s data caching and use it for efficient data transfer
- Write/run standalone Spark programs with the Spark API
- Use Spark Structured Streaming to process streaming (real-time) data
- Ingest streaming data from Kafka, and process via Spark Structured Streaming
- Understand performance implications and optimizations when using Spark
Prerequisites
Participants should have basic programming experience, preferably in Python; no prior experience with Spark is required.