Hands-on Guide to Apache Spark 3

This book explains how to scale Apache Spark 3 to handle massive amounts of data, either via batch or streaming processing. It covers how to use Spark’s structured APIs to perform complex data transformations and analyses you can use to implement end-to-end analytics workflows.

This book covers Spark 3's new features, theoretical foundations, and application architecture. The first section introduces the Apache Spark ecosystem as a unified engine for large scale data analytics, and shows you how to run and fine-tune your first application in Spark. The second section centers on batch processing suited to end-of-cycle processing, and data ingestion through files and databases. It explains Spark DataFrame API as well as structured and unstructured data with Apache Spark. The last section deals with scalable, high-throughput, fault-tolerant streaming processing workloads to process real-time data. Here you'll learn about Apache Spark Streaming’s execution model, the architecture of Spark Streaming, monitoring, reporting, and recovering Spark streaming. A full chapter is devoted to future directions for Spark Streaming. With real-world use cases, code snippets, and notebooks hosted on GitHub, this book will give you an understanding of large-scale data analysis concepts--and help you put them to use.

Upon completing this book, you will have the knowledge and skills to seamlessly implement large-scale batch and streaming workloads to analyze real-time data streams with Apache Spark.

WHAT YOU WILL LEARN

* Master the concepts of Spark clusters and batch data processing
* Understand data ingestion, transformation, and data storage
* Gain insight into essential stream processing concepts and different streaming architectures
* Implement streaming jobs and applications with Spark Streaming

WHO THIS BOOK IS FOR

Data engineers, data analysts, machine learning engineers, Python and R programmersALFONSO ANTOLÍNEZ GARCÍA is a senior IT manager with a long professional career serving in several multinational companies such as Bertelsmann SE, Lafarge, and TUI AG. He has been working in the media industry, the building materials industry, and the leisure industry. Alfonso also works as a university professor, teaching artificial intelligence, machine learning, and data science. In his spare time, he writes research papers on artificial intelligence, mathematics, physics, and the applications of information theory to other sciences. Part I. Apache Spark Batch Data Processing

Chapter 1: Introduction to Apache Spark for Large-Scale Data Analytics

1.1. What is Apache Spark?

1.2. Spark Unified Analytics

1.3. Batch vs Streaming Data1.4. Spark Ecosystem

Chapter 2: Getting Started with Apache Spark

2.2. Scala and PySpark Interfaces

2.3. Spark Application Concepts2.4. Transformations and Actions in Apache Spark

2.5. Lazy Evaluation in Apache Spark

2.6. First Application in Spark

2.7. Apache Spark Web UI

Chapter 3: Spark Dataframe API

Chapter 4: Spark Dataset API

Chapter 5: Structured and Unstructured Data with Apache Spark

5.1. Data Sources

5.2. Generic Load/Save Functions

5.3. Generic File Source Options

5.4. Parquet Files

5.5. ORC Files

5.6. JSON Files

5.7. CSV Files

5.8. Text Files

5.9. Hive Tables5.10. JDBC To Other Databases

Chapter 6: Spark Machine Learning with MLlib

Part II. Spark Data Streaming

Chapter 7: Introduction to Apache Spark Streaming

7.1. Apache Spark Streaming’s Execution Model

7.2. Stream Processing Architectures

7.3. Architecture of Spark Streaming: Discretized Streams

7.4. Benefits of Discretized Stream Processing

7.4.1. Dynamic Load Balancing

7.4.2. Fast Failure and Straggler Recovery

Chapter 8: Structured Streaming

8.1. Streaming Analytics

8.2. Connecting to a Stream

8.3. Preparing the Data in a Stream

8.4. Operations on a Streaming Dataset

Chapter 9: Structured Streaming Sources

9.1. File Sources

9.2. Apache Kafka Source

9.3. A Rate Source

Chapter 10: Structured Streaming Sinks

10.1. Output Modes

10.2. Output Sinks

10.3. File Sink

10.4. The Kafka Sink

10.5. The Memory Sink

10.6. Streaming Table APIs

10.7. Triggers

10.8. Managing Streaming Queries

10.9. Monitoring Streaming Queries

10.9.1. Reading Metrics Interactively

10.9.2. Reporting Metrics programmatically using Asynchronous APIs

10.9.3. Reporting Metrics using Dropwizard

10.9.4. Recovering from Failures with Checkpointing

10.9.5. Recovery Semantics after Changes in a Streaming Query

Chapter 11: Future Directions for Spark Streaming

11.1. Backpressure

11.2. Dynamic Scaling

11.3. Event time and out-of-order data

11.4. UI enhancements

11.5. Continuous Processing

Chapter 12: Watermarks. A deep survey of temporal progress metrics