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

Defining droppable data in Beam

This section will be a short return to the material we covered in Chapter 2, Implementing, Testing, and Deploying Basic Pipelines, where we already defined what late data means. To recap – late data is every data element that has a timestamp that is behind the watermark. That is to say, the watermark tells us that we should not receive a data element with a timestamp lower than the watermark, but nevertheless, we do receive such an element. This is perfectly fine, and as already described in Chapter 1, Introduction to Data Processing with Apache Beam, a perfect watermark would introduce unnecessary – or even impractical – latency. However, what we left unanswered is the following question – what happens to data elements that arrive too late? We know that we can define allowed lateness, but what if any data arrives even later? And as always, the answer is – it depends. Luckily, some of the concepts relating to streaming...