Building Big Data Pipelines with Apache Beam: Use a single programming model for both batch and stream data processing
Author: Jan Lukavský
Publisher finelybook 出版社: Packt Publishing (January 21,2022)
Language 语言: English
Print Length 页数: 342 pages
ISBN-10: 1800564937
ISBN-13: 9781800564930
Book Description
Implement,run,operate,and test data processing pipelines using Apache Beam
Key Features
Understand how to improve usability and productivity when implementing Beam pipelines
Learn how to use stateful processing to implement complex use cases using Apache Beam
Implement,test,and run Apache Beam pipelines with the help of expert tips and techniques
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.
Author: 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.
What you will learn
Understand the core concepts and architecture of Apache Beam
Implement stateless and stateful data processing pipelines
Use state and timers for processing real-time event processing
Structure your code for reusability
Use streaming SQL to process real-time data for increasing productivity and data accessibility
Run a pipeline using a portable runner and implement data processing using the Apache Beam Python SDK
Implement Apache Beam I/O connectors using the Splittable DoFn API
