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

Summary

In this chapter, you have learned one of the most important features of production data pipelines: version control. A software developer would not write code without using version control and neither should a data engineer. You have learned how to install and configure the Nifi Registry and how to start tracking version on processor groups. Lastly, you are now able to persist the version to GitHub. Any changes to your data pipelines will be saved and if you need to roll back, you can. As your team grows, all the data engineers will be able to manage the data pipelines and be sure they have the latest versions, all while developing locally.

In the next chapter, you will learn about logging and monitoring your data pipelines. If something goes wrong, and it will, you will need to know about it. Good logging and monitoring of data pipelines will allow you to catch errors when they happen and debug them to restore your data flows.