Book Image

Data Engineering with Python

By : Paul Crickard
Book Image

Data Engineering with Python

By: Paul Crickard

Overview of this book

Data engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python. The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You’ll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You’ll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you’ll build architectures on which you’ll learn how to deploy data pipelines. By the end of this Python book, you’ll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.
Table of Contents (21 chapters)
Section 1: Building Data Pipelines – Extract Transform, and Load
Section 2:Deploying Data Pipelines in Production
Section 3:Beyond Batch – Building Real-Time Data Pipelines


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.