You're reading from Data Engineering with Python

Product type Book

Published in Oct 2020

Publisher Packt

ISBN-13 9781839214189

Pages 356 pages

Edition 1st Edition

Languages

Python

Concepts

Data Analysis

Author (1):

Paul Crickard

Table of Contents (21) Chapters

Preface

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

2. Chapter 1: What is Data Engineering?

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

Building data pipelines in Apache Airflow

Apache Airflow uses Python functions, as well as Bash or other operators, to create tasks that can be combined into a Directed Acyclic Graph (DAG) – meaning each task moves in one direction when completed. Airflow allows you to combine Python functions to create tasks. You can specify the order in which the tasks will run, and which tasks depend on others. This order and dependency are what make it a DAG. Then, you can schedule your DAG in Airflow to specify when, and how frequently, your DAG should run. Using the Airflow GUI, you can monitor and manage your DAG. By using what you learned in the preceding sections, you will now make a data pipeline in Airflow.

Building a CSV to a JSON data pipeline

Starting with a simple DAG will help you understand how Airflow works and will help you to add more functions to build a better data pipeline. The DAG you build will print out a message using Bash, then read the CSV and print a list...