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 how to use the NiFi GUI to monitor your data pipelines using the status bar, the bulletin, and counters. You also learned how to add processors that can send information to you inside your data pipeline. With the PutSlack processor, you were able to send yourself direct messages when there was a failure, and you passed data from the flowfile in the message with the NiFi expression language. Lastly, you learned how to use the API to write your own monitoring tools and grab the same data as is in the NiFi GUI—even reading the contents of a single flowfile.

In the next chapter, you will learn how to deploy your production pipelines. You will learn how to use processor groups, templates, versions, and variables to allow you to import data pipelines to a production NiFi instance with minimal configuration.