Building ETL Pipelines with Python: Create and deploy enterprise-ready ETL pipelines by employing modern methods

Develop production-ready ETL pipelines by leveraging Python libraries and deploying them for suitable use cases
Key Features

Understand how to set up a Python virtual environment with PyCharm
Learn functional and object-oriented approaches to create ETL pipelines
Create robust CI/CD processes for ETL pipelines
Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Modern extract, transform, and load (ETL) pipelines for data engineering have favored the Python language for its broad range of uses and a large assortment of tools, applications, and open source components. With its simplicity and extensive library support, Python has emerged as the undisputed choice for data processing.

In this book, you’ll walk through the end-to-end process of ETL data pipeline development, starting with an introduction to the fundamentals of data pipelines and establishing a Python development environment to create pipelines. Once you've explored the ETL pipeline design principles and ET development process, you'll be equipped to design custom ETL pipelines. Next, you'll get to grips with the steps in the ETL process, which involves extracting valuable data; performing transformations, through cleaning, manipulation, and ensuring data integrity; and ultimately loading the processed data into storage systems. You’ll also review several ETL modules in Python, comparing their pros and cons when building data pipelines and leveraging cloud tools, such as AWS, to create scalable data pipelines. Lastly, you’ll learn about the concept of test-driven development for ETL pipelines to ensure safe deployments.

By the end of this book, you’ll have worked on several hands-on examples to create high-performance ETL pipelines to develop robust, scalable, and resilient environments using Python.
What you will learn

Explore the available libraries and tools to create ETL pipelines using Python
Write clean and resilient ETL code in Python that can be extended and easily scaled
Understand the best practices and design principles for creating ETL pipelines
Orchestrate the ETL process and scale the ETL pipeline effectively
Discover tools and services available in AWS for ETL pipelines
Understand different testing strategies and implement them with the ETL process

Who this book is for

If you are a data engineer or software professional looking to create enterprise-level ETL pipelines using Python, this book is for you. Fundamental knowledge of Python is a prerequisite.
Table of Contents

A Primer on Python and the Development Environment
Understanding the ETL Process and Data Pipelines
Design Principles for Creating Scalable and Resilient Pipelines
Sourcing Insightful Data and Data Extraction Strategies
Data Cleansing and Transformation
Loading Transformed Data
Tutorial – Building an End-to End ETL Pipeline in Python
Powerful ETL Libraries and Tools in Python
A Primer on AWS tools for ETL Processes
Tutorial – Creating an ETL Pipeline in AWS
Building Robust Deployment Pipelines in AWS
Orchestration and Scaling in ETL Pipelines
Testing Strategies for ETL pipelines
Best Practices for ETL Pipelines
Use Cases and Further Reading