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Building Big Data Pipelines with Apache Beam
Building Big Data Pipelines with Apache Beam

Building Big Data Pipelines with Apache Beam: Use a single programming model for both batch and stream data processing

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Building Big Data Pipelines with Apache Beam

Chapter 1: Introduction to Data Processing with Apache Beam

Data. Big data. Real-time data. Data streams. Many buzzwords to describe many things, and yet they have many common properties. Mind-blowing applications can be developed from the successful application of (theoretically) simple logic – take data and produce knowledge. However, a simple-sounding task can turn out to be difficult when the amount of data needed to produce knowledge is huge (and still growing). Given the vast volumes of data produced by humanity every day, which tools should we choose to turn our simple logic into scalable solutions? That is, solutions that protect our investment in creating the data extraction logic, even in the presence of new requirements arising or changing on a daily basis, and new data processing technologies being created? This book focuses on why Apache Beam might be a good solution to these challenges, and it will guide you through the Beam learning process.

In this chapter, we will cover the following topics:

  • Why Apache Beam?
  • Writing your first pipeline
  • Running a pipeline against streaming data
  • Exploring the key properties of Unbounded data
  • Measuring the event time progress inside data streams
  • Assigning data to windows
  • Unifying batch and streaming data processing

Technical requirements

In this chapter, we will introduce some elementary pipelines written using Beam's Java Software Development Kit (SDK).

We will use the code located in the GitHub repository for this book: https://github.com/PacktPublishing/Building-Big-Data-Pipelines-with-Apache-Beam.

We will also need the following tools to be installed:

  • Java Development Kit (JDK) 11 (possibly OpenJDK 11), with JAVA_HOME set appropriately
  • Git
  • Bash

    Important note

    Although it is possible to run many tools in this book using the Windows shell, we will focus on using Bash scripting only. We hope Windows users will be able to run Bash using virtualization or Windows Subsystem for Linux (or any similar technology).

First of all, we need to clone the repository:

  1. To do this, we create a suitable directory, and then we run the following command:
    $ git clone https://github.com/PacktPublishing/Building-Big-Data-Pipelines-with-Apache-Beam.git
  2. This will result in a directory, Building-Big-Data-Pipelines-with-Apache-Beam, being created in the working directory. We then run the following command in this newly created directory:
    $ ./mvnw clean install 

Throughout this book, the $ character will denote a Bash shell. Therefore, $ ./mvnw clean install would mean to run the ./mvnw command in the top-level directory of the git clone (that is, Building-Big-Data-Pipelines-with-Apache-Beam). By using chapter1$ ../mvnw clean install, we mean to run the specified command in the subdirectory called chapter1.

Why Apache Beam?

There are two basic questions we might ask when considering a new technology to learn and apply in practice:

  • What problem am I struggling with that the new technology can help me solve?
  • What would the costs associated with the technology be?

Every sound technology has a well-defined selling point – that is, something that justifies its existence in the presence of competing technologies. In the case of Beam, this selling point could be reduced to a single word: portability. Beam is portable on several layers:

  • Beam's pipelines are portable between multiple runners (that is, a technology that executes the distributed computation described by a pipeline's author).
  • Beam's data processing model is portable between various programming languages.
  • Beam's data processing logic is portable between bounded and unbounded data.

Each of these points deserves a few words of explanation. By runner portability, we mean the possibility to run existing pipelines written in one of the supported programming languages (for instance, Java, Python, Go, Scala, or even SQL) against a data processing engine that can be chosen at runtime. A typical example of a runner would be Apache Flink, Apache Spark, or Google Cloud Dataflow. However, Beam is by no means limited to these; new runners are created as new technologies arise, and it's very likely that many more will be developed.

When we say Beam's data processing model is portable between various programming languages, we mean it has the ability to provide support for multiple SDKs, regardless of the language or technology used by the runner. This way, we can code Beam pipelines in the Go language, and then run these against the Apache Flink Runner, written in Java.

Last but not least, the core of Apache Beam's model is designed so that it is portable between bounded and unbounded data. Bounded data is what was historically called batch processing, while unbounded data refers to real-time processing (that is, an application crunching live data as it arrives in the system and producing a low-latency output).

Putting these pieces together, we can describe Beam as a tool that lets you deal with your big data architecture with the following vision:

Choose your preferred language, write your data processing pipeline, run this pipeline using a runner of your choice, and do all of this for both batch and real-time data at the same time.

Because everything comes at a price, you should expect to pay for flexibility like this – this price would be a somewhat bigger overhead in terms of CPU and/or memory usage. The Beam community works hard to make this overhead as small as possible, but the chances are that it will never be zero.

If all of this sounds compelling to you, then we are ready to start a journey exploring Apache Beam!

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Key benefits

  • 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

Description

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.

Who is this book for?

This book is for data engineers, data scientists, and data analysts who want to learn how Apache Beam works. Intermediate-level knowledge of the Java programming language is assumed.

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
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Length: 342 pages
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Table of Contents

12 Chapters
Section 1 Apache Beam: Essentials Chevron down icon Chevron up icon
Chapter 1: Introduction to Data Processing with Apache Beam Chevron down icon Chevron up icon
Chapter 2: Implementing, Testing, and Deploying Basic Pipelines Chevron down icon Chevron up icon
Chapter 3: Implementing Pipelines Using Stateful Processing Chevron down icon Chevron up icon
Section 2 Apache Beam: Toward Improving Usability Chevron down icon Chevron up icon
Chapter 4: Structuring Code for Reusability Chevron down icon Chevron up icon
Chapter 5: Using SQL for Pipeline Implementation Chevron down icon Chevron up icon
Chapter 6: Using Your Preferred Language with Portability Chevron down icon Chevron up icon
Section 3 Apache Beam: Advanced Concepts Chevron down icon Chevron up icon
Chapter 7: Extending Apache Beam's I/O Connectors Chevron down icon Chevron up icon
Chapter 8: Understanding How Runners Execute Pipelines Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(9 Ratings)
5 star 55.6%
4 star 11.1%
3 star 0%
2 star 11.1%
1 star 22.2%
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rafael Jan 21, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book impressed me a lot, I don't know if it is the first book that talks about this framework (Apache Beam), but it has helped me with some tips in my work.but I must also recognize that it is a very technical book and some experience is required to be able to understand it easily. It is not a book for someone who is starting in this world of Data Engineering, much less someone with little knowledge of Apache Beam. Although it explains very well and has good examples and graphics that help to understand, I think it is necessary to have at least an intermediate level on the subject.Therefore, I would like to highlight a few things about this experience:- Documented examples and with a repository on github, this is appreciated.- the book not only focuses on giving concepts, but also has many practical examples using other solutions such as kafka, apache flink or minikube, etc.- It is mentioned how to do unit tests on the constructed pipeline, but perhaps this is a point to improve by giving more depth to this topic.- Excellent explanation of the Use of Ptransform object GroupByKey, the graphics are also appreciated to understand this topic.- I liked how the book is sectioned- It is appreciated that there is information in both java programming language and python
Amazon Verified review Amazon
Antonio Cachuan Feb 22, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A great opportunity to increase your skill in doing Apache Beam pipelines. I think this book is a good pocket reference if you are consolidating your Beam knowledge. Let me share the pros and cons after reading it:Pros-The book starts by giving you an overview of all the requirements for setting up an environment, and all the examples (mainly using Java) are located in a repository. I was new to using Minikube but I was able to run the examples without major difficulties. -Continuous giving details about Beam concepts like PCollections, PTransform, and others. Here I have to admit that the explanation of the concept of Window helped me to finally clarify some personal doubts in this field.-I agree with how was presented the batch to the streaming world "batch is a special case of streaming" and the examples that followed support the author's idea.-All the chapters include examples and Jan explains line-by-line the main parts of his code.-In chapters 8 and 4, the author made a great work explaining all about Runners and PTransform respectively.Cons-I would like to get more advice for deploying a pipeline in production. For example, including tips and more troubleshooting or including a deployment sample in any cloud provider.-Not exactly a con, but you need some experience in Java, and some background developing pipelines.ConclusionsMy final thought is that if you want to go serious in Apache Beam purchasing this book is a no doubt decision.
Amazon Verified review Amazon
Joseph Jan 22, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As someone who has implemented an Apache Beam pipeline and actively works to maintain it weekly, I really wish I had this resource a year ago.Jan does a fantastic job explicating the reasons why Apache Beam exists in the first place, the foundational concepts underlying how it functions and provides comprehensive examples of how to use it effectively. Depending on your needs and technical acumen, some sections may not be as enticing or useful to you; however, that is where this book shines. By gradually building upon previous sections and increasing the level of granularity, two people with very disparate use cases can read until they have the information they need and no further.Due to the fact that I found the beam documentation lacking in explaining some of the foundational concepts, this book has been a great clarifier. I also appreciate the use of multiple languages since my pipelines are primarily built for purposes of data science and machine learning, where python dominates the landscape.Overall, I would absolutely recommend this to someone trying to start or further their career in data engineering. Thank you Jan for this fantastic resource!
Amazon Verified review Amazon
Amazon Customer Jan 24, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book goes over every topic intended for engineer who’s ready to get their hands dirty with Apache beam !I’ve used this book to review many of my base concepts and build code.This is a useful guide for Apache beam users!!
Amazon Verified review Amazon
Rachel Jun 15, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book explains Apache Beam distributed data processing system for batch and streaming processing, from introducing learning examples to diving deep into advanced topics.I liked that book provides Github repo with all you need to focus on learning Apache Beam.The first two chapters introduce foundational concepts for pipelines, data transforms, batch, streaming, how to think of event and processing times, and windowed processing. Next, book covers stateful processing that can be used for processing data with external RPC services, using side inputs to enrich data. While the initial introduction is using Java, then the book expands to using Apache Beam with SQL, Python, and a concept of cross-language pipelines.Advanced topics include exploring building your custom I/O using splittable DoFn, understanding how Beam runners execute pipelines at scale, observability etc. - this all helps to deeper understand Apache Beam and apply that to help optimize data pipelines.
Amazon Verified review Amazon
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