When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
This four-day hands-on training course delivers the key concepts and expertise developers need to use Apache Spark to develop high-performance parallel applications. Participants will learn how to...Read More
“The combination of Eric's lecture and the class materials enabled me to learn and practice the concepts went over in this training course.”
This four-day hands-on training course delivers the key concepts and expertise developers need to use Apache Spark to develop high-performance parallel applications. 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 “big data” 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.
This course is designed for developers and engineers who have programming experience, but prior knowledge of Hadoop and/or Spark is not required.
This course is designed for developers and engineers who have programming experience, but prior knowledge of Spark and Hadoop is not required. 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.
2. Introduction to Apache Hadoop and the Hadoop Ecosystem
3. Apache Hadoop File Storage
4. Distributed Processing on an Apache Hadoop Cluster
5. Apache Spark Basics
6. Working with DataFrames and Schemas
7. Analyzing Data with DataFrame Queries
8. RDD Overview
9. Transforming Data with RDDs
10. Aggregating Data with Pair RDDs
11. Querying Tables and Views with SQL
12. Working with Datasets in Scala
13. Writing, Configuring, and Running Spark Applications
14. Spark Distributed Processing
15. Distributed Data Persistence
16. Common Patterns in Spark Data Processing
17. Introduction to Structured Streaming
18. Structured Streaming with Apache Kafka
19. Aggregating and Joining Streaming DataFrames
A. Message Processing with Apache Kafka