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This course is a hands-on introduction to Apache Flink for Java and Scala developers who want to learn to build streaming applications.
After taking this course you will have learned enough about Flink's core concepts and the DataStream and SQL/Table APIs to be able to develop solutions for a wide variety of use cases, including data pipelines and ETL jobs, streaming analytics, and event-driven applications.
The programming exercises are oriented around building blocks used in common use cases, and will give you a chance to see how these APIs are used to solve real problems.
Beginner; no prior knowledge of Apache Flink is assumed.
Stream Processing and Apache Flink
Intro to the Streaming Runtime and Execution Model
Working with Keyed State
Checkpointing and Exactly-once Guarantees
Event Time and Watermarks
Process Functions and Timers
Dynamic Tables and Streaming SQL
Windows and Streaming Analytics
Streaming Joins
Using Kafka with Flink
Serialization
Testing
Hands-on with Keyed State
State Backends
Hands-on with Timers
Hands-on with Windows
Side Outputs
Hands-on with Flink SQL
Table/DataStream Integration
For the hands-on exercises you will need a computer with at least 8 GB RAM (MacOS, Linux, or Windows), with these tools installed:
Git
Java 8 or 11 JDK (a JRE is not sufficient)
An IDE for Java (or Scala) development (we recommend IntelliJ)
This is a remote, instructor-led training, spanning 4 sessions, each about 2 hours long. These instructor-led training sessions are not recorded, so please arrange your schedule so you can attend and participate.
Additional reading and hands-on exercises will be assigned as homework to be done between classes. This is a critical component of the course, and you should plan to spend at least a couple of hours after each session working with these self-paced materials. Give yourself extra time if you aren’t very familiar with Java (or Scala).
All of the slides will be shared with you.
This course is a hands-on introduction to key topics relating to putting Flink applications into production. The focus is on the Flink runtime, including its architecture, state backends, networking, and checkpointing, with an eye toward understanding how Flink works, and what can go wrong. You’ll learn what you can do to avoid problems, and how to diagnose and fix the issues you may experience, such as backpressure, checkpoint failures, and high latency.
The self-paced exercises provide a hands-on introduction to these same topics.
Intermediate
You should already have a general understanding of event time and watermarking, and familiarity with the basics of the DataStream API (at the level covered in our Apache Flink Developer Training). The examples and exercises all use the DataStream API, and are implemented in Java. They require recognizing what the provided applications are doing wrong and adjusting the configuration and/or the code so that they perform better.
State Backends
Tuning Serialization
Backpressure
Optimizing Latency and Throughput
Fault Tolerance, Tuning and Troubleshooting Checkpointing
Mitigating Skew
Scaling State with RocksDB
Application Evolution
The Flink Runtime
Metrics for Monitoring and Troubleshooting
Debugging Job Failures
Troubleshooting Watermarks
Tuning for Latency
Tuning for Throughput
Troubleshooting Checkpoints
For the hands-on exercises you will need a computer with at least 8 GB RAM (MacOS, Linux, or Windows), with these tools installed:
Git
Java 8 or 11 JDK (a JRE is not sufficient)
An IDE for Java development (we recommend IntelliJ)
This is a remote, instructor-led training, delivered in two 2-hour sessions. These instructor-led training sessions are not recorded, so please arrange your schedule so you can attend and participate.
Additional reading and hands-on exercises will be assigned as homework to be done between classes. This is a critical component of the course, and you should plan to spend several hours between the sessions working with these self-paced materials.
All of the slides will be shared with you.
Online course format
Online course format
