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This course delivers the key concepts and expertise developers need to develop high-performance parallel applications with Apache Spark 2. Participants will learn how to use Spark SQL to query structured data and Spark Streaming to perform real-time processing on streaming data from a variety of sources. Developers will also practice writing applications that use core Spark to perform ETL processing and iterative algorithms. The course covers how to work with large datasets stored in a distributed file system, and execute Spark applications on a Hadoop cluster. After taking this course, participants will be prepared to face real-world challenges and build applications to execute faster decisions, better decisions, and interactive analysis, applied to a wide variety of use cases, architectures, and industries.
The prerequisites for this course are:
- Apache Spark examples and hands-on exercises are presented in Scala and Python. The ability to program in one of those languages is required.
- Basic familiarity with the Linux command line is assumed.
- Basic knowledge of SQL is helpful.
4 Days/Lecture Lab
This course is designed for developers and engineers who have programming experience, but prior knowledge of Hadoop and/or Spark is not required
- Introduction to Apache Hadoop and the Hadoop Ecosystem
- Apache Hadoop File Storage
- Distributed Processing on an Apache Hadoop Cluster
- Apache Spark Basics
- Working with DataFrames and Schemas
- Analyzing Data with DataFrame Queries
- RDD Overview
- Transforming Data with RDDs
- Aggregating Data with Pair RDDs
- Querying Tables and Views with Apache Spark SQL
- Working with Datasets in Scala
- Writing, Configuring, and Running Apache Spark Applications
- Distributed Processing
- Distributed Data Persistence
- Apache Spark Streaming: Introduction to DStreams
- Apache Spark Streaming: Processing Multiple Batches
- Apache Spark Streaming: Data Sources