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

Chapter 5: Cleaning, Transforming, and Enriching Data

In the previous two chapters, you learned how to build data pipelines that could read and write from files and databases. In many instances, these skills alone will enable you to build production data pipelines. For example, you will read files from a data lake and insert them into a database. You now have the skills to accomplish this. Sometimes, however, you will need to do something with the data after extraction but prior to loading. What you will need to do is clean the data. Cleaning is a vague term. More specifically, you will need to check the validity of the data and answer questions such as the following: Is it complete? Are the values within the proper ranges? Are the columns the proper type? Are all the columns useful?

In this chapter, you will learn the basic skills needed to perform exploratory data analysis. Once you have an understanding of the data, you will use that knowledge to fix common data problems that...