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  • Book Overview & Buying Building Big Data Pipelines with Apache Beam
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Building Big Data Pipelines with Apache Beam

Building Big Data Pipelines with Apache Beam

By : Lukavský
3.7 (9)
Buy this Book
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Building Big Data Pipelines with Apache Beam

Building Big Data Pipelines with Apache Beam

3.7 (9)
By: Lukavský
Buy this Book

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)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Download the example code files
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Download the color images
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Conventions used
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Get in touch
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Share Your Thoughts
1
Section 1 Apache Beam: Essentials
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Section 1 Apache Beam: Essentials
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2
Chapter 1: Introduction to Data Processing with Apache Beam
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Chapter 1: Introduction to Data Processing with Apache Beam
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Technical requirements
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Why Apache Beam?
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Writing your first pipeline
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Running our pipeline against streaming data
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Exploring the key properties of unbounded data
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Measuring event time progress inside data streams
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Assigning data to windows
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Unifying batch and streaming data processing
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Summary
3
Chapter 2: Implementing, Testing, and Deploying Basic Pipelines
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Chapter 2: Implementing, Testing, and Deploying Basic Pipelines
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Technical requirements
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Setting up the environment for this book
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Task 1 – Calculating the K most frequent words in a stream of lines of text
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Task 2 – Calculating the maximal length of a word in a stream
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Specifying the PCollection Coder object and the TypeDescriptor object
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Understanding default triggers, on time, and closing behavior
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Introducing the primitive PTransform object – Combine
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Task 3 – Calculating the average length of words in a stream
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Task 4 – Calculating the average length of words in a stream with fixed lookback
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Ensuring pipeline upgradability
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Task 5 – Calculating performance statistics for a sport activity tracking application
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Introducing the primitive PTransform object – GroupByKey
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Introducing the primitive PTransform object – Partition
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Summary
4
Chapter 3: Implementing Pipelines Using Stateful Processing
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Chapter 3: Implementing Pipelines Using Stateful Processing
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Technical requirements
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Task 6 – Using an external service for data augmentation
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Introducing the primitive PTransform object – stateless ParDo
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Task 7 – Batching queries to an external RPC service
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Task 8 – Batching queries to an external RPC service with defined batch sizes
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Introducing the primitive PTransform object – stateful ParDo
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Using side outputs
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Defining droppable data in Beam
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Task 9 – Separating droppable data from the rest of the data processing
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Task 10 – Separating droppable data from the rest of the data processing, part 2
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Using side inputs
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Summary
5
Section 2 Apache Beam: Toward Improving Usability
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Section 2 Apache Beam: Toward Improving Usability
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Chapter 4: Structuring Code for Reusability
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Chapter 4: Structuring Code for Reusability
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Technical requirements
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Explaining PTransform expansion
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Task 11 – Enhancing SportTracker by runner motivation using side inputs
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Introducing composite transform – CoGroupByKey
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Task 12 – enhancing SportTracker by runner motivation using CoGroupByKey
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Introducing the Join library DSL
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Stream-to-stream joins explained
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Task 13 – Writing a reusable PTransform – StreamingInnerJoin
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Table-stream duality
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Summary
7
Chapter 5: Using SQL for Pipeline Implementation
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Chapter 5: Using SQL for Pipeline Implementation
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Technical requirements
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Understanding schemas
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Implementing our first streaming pipeline using SQL
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Task 14 – Implementing SQLMaxWordLength
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Task 15 – Implementing SchemaSportTracker
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Task 16 – Implementing SQLSportTrackerMotivation
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Further development of Apache Beam SQL
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Summary
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Chapter 6: Using Your Preferred Language with Portability
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Chapter 6: Using Your Preferred Language with Portability
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Technical requirements
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Introducing the portability layer
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Implementing our first pipelines in the Python SDK
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Task 17 – Implementing MaxWordLength in the Python SDK
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Python SDK type hints and coders
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Task 18 – Implementing SportTracker in the Python SDK
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Task 19 – Implementing RPCParDo in the Python SDK
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Task 20 – Implementing SportTrackerMotivation in the Python SDK
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Using the DataFrame API
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Interactive programming using InteractiveRunner
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Introducing and using cross-language pipelines
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Summary
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Section 3 Apache Beam: Advanced Concepts
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Section 3 Apache Beam: Advanced Concepts
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Chapter 7: Extending Apache Beam's I/O Connectors
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Chapter 7: Extending Apache Beam's I/O Connectors
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Technical requirements
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Defining splittable DoFn as a unification for bounded and unbounded sources
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Task 21 – Implementing our own splittable DoFn – a streaming file source
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Task 22 – A non-I/O application of splittable DoFn – PiSampler
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The legacy Source API and the Read transform
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Writing a custom data sink
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Summary
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Chapter 8: Understanding How Runners Execute Pipelines
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Chapter 8: Understanding How Runners Execute Pipelines
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Describing the anatomy of an Apache Beam runner
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Explaining the differences between classic and portable runners
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Understanding how a runner handles state
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Exploring the Apache Beam capability matrix
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Understanding windowing semantics in depth
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Debugging pipelines and using Apache Beam metrics for observability
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Summary
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Why subscribe?
12
Other Books You May Enjoy
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Python SDK type hints and coders

Python3 can provide type hints on functions, as shown in the following code:

def toKv(s: str) -> beam.typehints.KV[bytes, bytes]:
  return ("".encode("utf-8"), s.encode("utf-8"))

The previous code defines a method called toKv that takes a string (str) as input and outputs an object that's compatible with beam.typehints.KV[bytes, bytes]. When we use such a function in a simple transform such as beam.Map(toKv), Beam can infer the type of the resulting PCollection and can automatically use a special-purpose coder instead of pickle. In the case of bytes, this would be ByteArrayCoder.

Besides declaring type hints to mapping functions, we can use a decorator for DoFns, which will declare the (input or output) type hint explicitly for the whole transform:

@beam.typehints.with_input_types(
    beam.typehints.Tuple[
        str, beam.typehints...
CONTINUE READING
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Tech Concepts
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Programming languages
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Building Big Data Pipelines with Apache Beam
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