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Building ETL Pipelines with Python
Building ETL Pipelines with Python

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

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Profile Icon Brij Kishore Pandey Profile Icon Emily Ro Schoof
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₱1785.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3 (6 Ratings)
Paperback Sep 2023 246 pages 1st Edition
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Arrow left icon
Profile Icon Brij Kishore Pandey Profile Icon Emily Ro Schoof
Arrow right icon
₱1785.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3 (6 Ratings)
Paperback Sep 2023 246 pages 1st Edition
eBook
₱1285.99 ₱1428.99
Paperback
₱1785.99
Subscription
Free Trial
eBook
₱1285.99 ₱1428.99
Paperback
₱1785.99
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Free Trial

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Building ETL Pipelines with Python

A Primer on Python and the Development Environment

Whether your production environment caters to only one data pipeline at a time or a whole multitude of overlapping systems, the core tenets of data environment management remain the same.

We have dedicated this chapter to breaking down the foundational roots of all successful applications by discussing the basic principles of the Python programming language and how utilizing package management applications can create clean, flexible, and reproducible development environments. We will walk you through a step-by-step tutorial on how to install and establish a basic Git-tracked development environment that will prevent future confounding modular incompatibilities from impacting the successful deployment of your data pipelines in production.

By the end of this chapter, you will have a strong understanding of why Python is a powerful tool that can be used to develop highly-customized and powerful data transformation ecosystems. We will cover the following topics:

  • Python fundamentals
  • Using Python attributes to build an application’s foundation
  • Key attributes of an effective development environment
  • Downloading and installing a local integrated development environment (IDE)
  • Creating and cloning a Git-tracked repository into your IDE
  • Managing project packages and circular dependencies with a module management system (MMS)
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Key benefits

  • 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

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.

Who is this book 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.

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
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Publication date : Sep 29, 2023
Length: 246 pages
Edition : 1st
Language : English
ISBN-13 : 9781804615256
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Product Details

Publication date : Sep 29, 2023
Length: 246 pages
Edition : 1st
Language : English
ISBN-13 : 9781804615256
Vendor :
Amazon
Category :
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Table of Contents

21 Chapters
Part 1:Introduction to ETL, Data Pipelines, and Design Principles Chevron down icon Chevron up icon
Chapter 1: A Primer on Python and the Development Environment Chevron down icon Chevron up icon
Chapter 2: Understanding the ETL Process and Data Pipelines Chevron down icon Chevron up icon
Chapter 3: Design Principles for Creating Scalable and Resilient Pipelines Chevron down icon Chevron up icon
Part 2:Designing ETL Pipelines with Python Chevron down icon Chevron up icon
Chapter 4: Sourcing Insightful Data and Data Extraction Strategies Chevron down icon Chevron up icon
Chapter 5: Data Cleansing and Transformation Chevron down icon Chevron up icon
Chapter 6: Loading Transformed Data Chevron down icon Chevron up icon
Chapter 7: Tutorial – Building an End-to-End ETL Pipeline in Python Chevron down icon Chevron up icon
Chapter 8: Powerful ETL Libraries and Tools in Python Chevron down icon Chevron up icon
Part 3:Creating ETL Pipelines in AWS Chevron down icon Chevron up icon
Chapter 9: A Primer on AWS Tools for ETL Processes Chevron down icon Chevron up icon
Chapter 10: Tutorial – Creating an ETL Pipeline in AWS Chevron down icon Chevron up icon
Chapter 11: Building Robust Deployment Pipelines in AWS Chevron down icon Chevron up icon
Part 4:Automating and Scaling ETL Pipelines Chevron down icon Chevron up icon
Chapter 12: Orchestration and Scaling in ETL Pipelines Chevron down icon Chevron up icon
Chapter 13: Testing Strategies for ETL Pipelines Chevron down icon Chevron up icon
Chapter 14: Best Practices for ETL Pipelines Chevron down icon Chevron up icon
Chapter 15: Use Cases and Further Reading Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
(6 Ratings)
5 star 83.3%
4 star 0%
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2 star 0%
1 star 16.7%
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Data Science Leader Oct 18, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Whether you're a data scientist, data engineer, or machine learning engineer, this book is a goldmine. It breaks down the world of ETL in a way that's easy to get. Brij Kishore Pandey and Emily Schoof make it simple:They explain the difference between ETL and ELT.They share ETL design patterns.They give tips for every ETL step.I loved how they focus on doing things. They show you how to set up an ETL pipeline with Python and also with AWS tools like S3, Lambda, and Step Functions. Plus, they cover how to test your pipelines and keep everything running smooth.With so many companies using cloud tools, the bits about AWS in this book are a big win. You'll feel way more confident using these tools at work.All in all, this book is top-notch for anyone in the data world looking to step up their game. Don't miss out!
Amazon Verified review Amazon
Amir Esmaeili Nov 15, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Einfach Super!
Amazon Verified review Amazon
H2N Oct 18, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
An insightful read for data scientists, engineers, and data enthusiasts. This book provides a comprehensive overview of ETL data pipelines in Python, distinguishing between ETL and ELT. It delves deep into the design principles essential for scalable and robust pipelines and offers strategies for data sourcing and extraction. The hands-on tutorials present an end-to-end ETL pipeline, both in Python and AWS, shedding light on the associated AWS tools. What sets it apart is its discussion on the limitations of ETL pipelines and effective scaling techniques to meet growing demands. A must-read for those eager to enhance their data pipeline knowledge.
Amazon Verified review Amazon
Deblina Dec 02, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
If you are looking for a book on how to build enterprise-ready ETL pipelines with python, you should check out this book.This book is a comprehensive guide that covers everything from data extraction, transformation, loading, testing, and deployment. You will learn how to use popular python libraries such as pandas, sqlalchemy, airflow, and luigi to create scalable and reliable data pipelines. You will also get practical tips and best practices on how to design, document, and monitor your pipelines.The book is full of examples and exercises that will help you apply the concepts to real-world scenarios. Whether you are a beginner or an expert in ETL, this book will help you take your skills to the next level.
Amazon Verified review Amazon
Om S Oct 17, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book guides you through the process of creating data pipelines using Python for real-world applications. You'll learn how to set up a Python environment, use both functional and object-oriented approaches, and build robust CI/CD processes.The book covers the fundamentals of ETL pipelines, from data extraction and cleansing to loading data. It also delves into various Python libraries and tools, including AWS services, for creating efficient pipelines. You'll discover testing strategies and best practices to ensure reliability.If you're a data engineer or software professional with basic Python knowledge, this book is for you. It provides practical insights and hands-on examples to develop high-performance ETL pipelines in Python.
Amazon Verified review Amazon
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