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

Describing the anatomy of an Apache Beam runner

Let's first take a look at the typical life cycle of a pipeline, from the construction time to the pipeline teardown. The complete life cycle is illustrated in the following figure:

Figure 8.1 – The execution of a pipeline by a runner

The pipeline construction is already well known – we spent most of this book showing how to construct and test pipelines. The next step is submitting the pipeline to a runner. This is the point where the pipeline crosses the SDK-runner boundary, typically by a call to Pipeline.run().

After the pipeline is submitted to the runner, the runner proceeds as follows:

  1. Once a runner receives a pipeline, it first performs pipeline validation. This consists of various runner-independent validations – for instance, validating that an appropriate window function and/or trigger is being set and depending on the boundedness of the inputs of the pipeline. These...