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Building ETL Pipelines with Python

By Brij Kishore Pandey , Emily Ro Schoof
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  1. Free Chapter
    Chapter 1: A Primer on Python and the Development Environment
About this book
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.
Publication date:
September 2023
Publisher
Packt
Pages
246
ISBN
9781804615256

 

Part 1:Introduction to ETL, Data Pipelines, and Design Principles

For the first part of this book, we will introduce the fundamentals of data pipelines in Python and set up your local development environment with Integrated Development Environments (IDEs), virtual environments, and Git version control. We will provide you with an overview of what Extract-Load-Transform (ETL) data pipelines are and how to design them yourself. As a word of caution, Python is at the core of this book; you must have a basic familiarity with Python in order to follow along accordingly.

This section contains the following chapters:

  • Chapter 1, A Primer on Python and the Development Environment
  • Chapter 2, Understanding the ETL Process and Data Pipelines
  • Chapter 3, Design Principles for Creating Scalable and Resilient Pipelines
About the Authors
  • Brij Kishore Pandey

    Brij Kishore Pandey stands as a testament to dedication, innovation, and mastery in the vast domains of software engineering, data engineering, machine learning, and architectural design. His illustrious career, spanning over 14 years, has seen him wear multiple hats, transitioning seamlessly between roles and consistently pushing the boundaries of technological advancement. He has a degree in electrical and electronics engineering. His work history includes the likes of JP Morgan Chase, American Express, 3M Company, Alaska Airlines, and Cigna Healthcare. He is currently working as a principal software engineer at Automatic Data Processing Inc. (ADP). Originally from India, he resides in Parsippany, New Jersey, with his wife and daughter.

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  • Emily Ro Schoof

    Emily Ro Schoof is a dedicated data specialist with a global perspective, showcasing her expertise as a data scientist and data engineer on both national and international platforms. Drawing from a background rooted in healthcare and experimental design, she brings a unique perspective of expertise to her data analytic roles. Emily's multifaceted career ranges from working with UNICEF to design automated forecasting algorithms to identify conflict anomalies using near real-time media monitoring to serving as a subject matter expert for General Assembly's Data Engineering course content and design. Her mission is to empower individuals to leverage data for positive impact. Emily holds the strong belief that providing easy access to resources that merge theory and real-world applications is the essential first step in this process.

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