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 learned how to process CSV and JSON files using Python. Using this new skill, you have created a data pipeline in Apache Airflow by creating a Python function to process a CSV and transform it into JSON. You should now have a basic understanding of the Airflow GUI and how to run DAGs. You also learned how to build data pipelines in Apache NiFi using processors. The process for building more advanced data pipelines is the same, and you will learn the skills needed to accomplish this throughout the rest of this book.

In the next chapter, you will learn how to use Python, Airflow, and NiFi to read and write data to databases. You will learn how to use PostgreSQL and Elasticsearch. Using both will expose you to standard relational databases that can be queried using SQL and NoSQL databases that allow you to store documents and use their own query languages.