Book Image

Building Big Data Pipelines with Apache Beam

By : Jan Lukavský
Book Image

Building Big Data Pipelines with Apache Beam

By: Jan Lukavský

Overview of this book

Apache Beam is an open source unified programming model for implementing and executing data processing pipelines, including Extract, Transform, and Load (ETL), batch, and stream processing. This book will help you to confidently build data processing pipelines with Apache Beam. You’ll start with an overview of Apache Beam and understand how to use it to implement basic pipelines. You’ll also learn how to test and run the pipelines efficiently. As you progress, you’ll explore how to structure your code for reusability and also use various Domain Specific Languages (DSLs). Later chapters will show you how to use schemas and query your data using (streaming) SQL. Finally, you’ll understand advanced Apache Beam concepts, such as implementing your own I/O connectors. By the end of this book, you’ll have gained a deep understanding of the Apache Beam model and be able to apply it to solve problems.
Table of Contents (13 chapters)
1
Section 1 Apache Beam: Essentials
5
Section 2 Apache Beam: Toward Improving Usability
9
Section 3 Apache Beam: Advanced Concepts

Summary

In this chapter, we learned about all the remaining primitive transforms. We now know the details of both the stateless and stateful ParDo objects. We know the basic life cycle of DoFn and understand the concept of bundles. We understand why input to stateful ParDo objects has to be in the form of keyed PCollection objects. We have seen and understood the details of how states and timers are managed by Beam and how they are delegated to runners in order to ensure fault tolerance. We know how a watermark propagates in transforms in general and what the (stateful) transform's input watermark and output watermark are. We have successfully used our knowledge to create our version of the GroupIntoBatches transform, which stores data into states before delegating them to an external RPC service.

Next, we focused on handling late and droppable data to be able to avoid data loss. We created one simple and one sophisticated version of a transform process to filter (split) data...