Overview
This four-day hands-on training course teaches the key concepts and knowledge developers need to use Apache Spark in developing high-performance, parallel applications on the Cloudera Data Platform (CDP).
Hands-on exercises allow students to practice writing Spark applications that integrate with CDP core components, such as Hive and Kafka. Participants will learn how to use Spark SQL to query structured data, use Spark Streaming to perform real-time processing on streaming data, and work with “big data” stored in a distributed file system.
After taking this course, participants will be prepared to face real-world challenges and build applications that make fast and relevant decisions, implementing interactive analysis applied to a wide variety of use cases, architectures, and industries.
What you'll learn
Through instructor-led discussion and interactive, hands-on exercises, you will learn how to:
- Distribute, store, and process data in a CDP cluster
- Write, configure, and deploy Apache Spark applications
- Use Spark interpreters and Spark applications to explore, process, and analyze distributed data
- Query data using Spark SQL, DataFrames, and Hive tables
- Use Spark Streaming together with Kafka to process a data stream
What to expect
This course is designed for developers and data engineers. All students are expected to have basic Linux experience and basic proficiency with either Python or Scala programming languages. Basic knowledge of SQL is helpful. Prior knowledge of Spark and Hadoop is not required
Book the course
To register for a public class, please contact schedule-training@cloudera.com.
Course Details
Introduction to Zeppelin
- Why Notebooks?
- Zeppelin Notes
- Demo: Apache Spark In 5 Minutes
HDFS Introduction
- HDFS Overview
- HDFS Components and Interactions
- Additional HDFS Interactions
- Ozone Overview
- Exercise: Working with HDFS
YARN Introduction
- YARN Overview
- YARN Components and Interaction
- Working with YARN
- Exercise: Working with YARN
Distributed Processing History
- The Disk Years: 2000 ->2010
- The Memory Years: 2010 ->2020
- The GPU Years: 2020 ->
Working with DataFrames
- Introduction to DataFrames
- Exercise: Introducing DataFrames
- Exercise: Reading and Writing DataFrames
- Exercise: Working with Columns
- Exercise: Working with Complex Types
- Exercise: Working with Complex Types
- Exercise: Combining and Splitting DataFrames
- Exercise: Summarizing and Grouping DataFrames
- Exercise: Working with UDFs
- Exercise: Working with Windows
Introduction to Apache Hive
- About Hive
Hive and Spark Integration
- Hive and Spark Integration
- Exercise: Spark Integration with Hive
Data Visualization with Zeppelin
- Introduction to Data Visualization with Zeppelin
- Zeppelin Analytics
- Zeppelin Collaboration
- Exercise: AdventureWorks
Distributed Processing Challenges
- Shuffle
- Skew
- Order
Spark Distributed Processing
- Spark Distributed Processing
- Exercise: Explore Query Execution Order
Spark Distributed Persistence
- DataFrame and Dataset Persistence
- Persistence Storage Levels
- Viewing Persisted RDDs
- Exercise: Persisting DataFrames
Writing, Configuring, and Running Spark Applications
- Writing a Spark Application
- Building and Running an Application
- Application Deployment Mode
- The Spark Application Web UI
- Configuring Application Properties
- Exercise: Writing, Configuring, and Running a Spark Application
Introduction to Structured Streaming
- Introduction to Structured Streaming
- Exercise: Processing Streaming Data
Message Processing with Apache Kafka
- What is Apache Kafka?
- Apache Kafka Overview
- Scaling Apache Kafka
- Apache Kafka Cluster Architecture
- Apache Kafka Command Line Tools
Structured Streaming with Apache Kafka
- Receiving Kafka Messages
- Sending Kafka Messages
- ·Exercise: Working with Kafka Streaming Messages
Aggregating and Joining Streaming DataFrames
- Streaming Aggregation
- Joining Streaming DataFrames
- Exercise: Aggregating and Joining Streaming DataFrames
Appendix: Working with Datasets in Scala
- Working with Datasets in Scala
- Exercise: Using Datasets in Scala
Learn more
CCA Spark and Hadoop Developer Certification
This course is excellent preparation for the CCA Spark and Hadoop Developer exam. Although we recommend further training and hands-on experience before attempting the exam, this course covers many of the subjects tested.
Certification is a great differentiator. It helps establish you as a leader in the field, providing employers and customers with tangible evidence of your skills and expertise.
Advance your career
Big data developers are among the world's most in-demand and highly-compensated technical roles. Check out some of the job opportunities currently listed that match the professional profile, many of which seek CCA qualifications.
Private training
We also provide private training at your site, at your pace, and tailored to your needs.