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

Writing and reading files in Python

The title of this section may sound strange as you are probably used to seeing it written as reading and writing, but in this section, you will write data to files first, then read it. By writing it, you will understand the structure of the data and you will know what it is you are trying to read.

To write data, you will use a library named faker. faker allows you to easily create fake data for common fields. You can generate an address by simply calling address(), or a female name using name_female(). This will simplify the creation of fake data while at the same time making it more realistic.

To install faker, you can use pip:

pip3 install faker

With faker now installed, you are ready to start writing files. The next section will start with CSV files.

Writing and reading CSVs

The most common file type you will encounter is Comma-Separated Values (CSV). A CSV is a file made up of fields separated by commas. Because commas are...