You're reading from Data Engineering with Python Work with massive datasets to design data models and automate data pipelines using Python

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781839214189

Length 356 pages

Edition 1st Edition

Languages

Python

Concepts

Data Analysis

Author (1):

Paul Crickard

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Table of Contents (21) Chapters

Preface

1. Section 1: Building Data Pipelines – Extract Transform, and Load

2. Chapter 1: What is Data Engineering? FREE CHAPTER

3. Chapter 2: Building Our Data Engineering Infrastructure

4. Chapter 3: Reading and Writing Files

5. Chapter 4: Working with Databases

6. Chapter 5: Cleaning, Transforming, and Enriching Data

7. Chapter 6: Building a 311 Data Pipeline

8. Section 2:Deploying Data Pipelines in Production

9. Chapter 7: Features of a Production Pipeline

10. Chapter 8: Version Control with the NiFi Registry

11. Chapter 9: Monitoring Data Pipelines

12. Chapter 10: Deploying Data Pipelines

13. Chapter 11: Building a Production Data Pipeline

14. Section 3:Beyond Batch – Building Real-Time Data Pipelines

15. Chapter 12: Building a Kafka Cluster

16. Chapter 13: Streaming Data with Apache Kafka

17. Chapter 14: Data Processing with Apache Spark

18. Chapter 15: Real-Time Edge Data with MiNiFi, Kafka, and Spark

19. Other Books You May Enjoy

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Appendix

Installing and running Spark

Apache Spark is a distributed data processing engine that can handle both streams and batch data, and even graphs. It has a core set of components and other libraries that are used to add functionality. A common depiction of the Spark ecosystem is shown in the following diagram:

Figure 14.1 – The Apache Spark ecosystem

To run Spark as a cluster, you have several options. Spark can run in a standalone mode, which uses a simple cluster manager provided by Spark. It can also run on an Amazon EC2 instance, using YARN, Mesos, or Kubernetes. In a production environment with a significant workload, you would probably not want to run in standalone mode; however, this is how we will stand up our cluster in this chapter. The principles will be the same, but the standalone cluster provides the fastest way to get you up and running without needing to dive into more complicated infrastructure.

To install Apache Spark, take the following...