About Data Engineering with Apache Beam
Takes a participant from no knowledge of Beam to being able to develop with Beam professionally. It covers the reasons why Beam is changing how we do data engineering. There is an in-depth coverage of Beam’s features and API. The class ends with a consideration of how to architect Big Data solutions with Beam and the Big Data ecosystem.
Duration: 1 day
Intended Audience: Technical, Software Engineers, QA, Analysts
Prerequisites: Intermediate-Level Java
You Will Learn
- What exists in the Big Data ecosystem so you can use the right tool for the right job.
- An understanding of how MapReduce works and how each phase works.
- What are Java 8 Lambdas and how they make your Beam code humanly readable.
- The basics of coding a Beam pipeline with Java to build your Big Data foundation.
- What is Avro, how it works with Beam, and how top data engineers use it to make maintainable and evolving data schemas.
- How Beam uses windows to make it easy to sessionize and trigger on time frames.
- How to integrate and use Beam with the rest of your Big Data systems.
Thinking in Big Data
Introducing Big Data
What is Beam?
Introduction to MapReduce
Getting Ready for Beam
Using Apache Maven
Coding With Beam
Beam API Pipelines
Beam API Processing
Avro and Beam
Beam and Avro
Choosing a runner
Beam and Ecosystem
- Apache Beam
- Apache Hadoop
- Apache Spark
- Apache Kafka