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Building ETL Pipelines with Python

You're reading from   Building ETL Pipelines with Python Create and deploy enterprise-ready ETL pipelines by employing modern methods

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781804615256
Length 246 pages
Edition 1st Edition
Languages
Tools
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Authors (2):
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Brij Kishore Pandey Brij Kishore Pandey
Author Profile Icon Brij Kishore Pandey
Brij Kishore Pandey
Emily Ro Schoof Emily Ro Schoof
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Emily Ro Schoof
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Toc

Table of Contents (22) Chapters Close

Preface 1. Part 1:Introduction to ETL, Data Pipelines, and Design Principles
2. Chapter 1: A Primer on Python and the Development Environment FREE CHAPTER 3. Chapter 2: Understanding the ETL Process and Data Pipelines 4. Chapter 3: Design Principles for Creating Scalable and Resilient Pipelines 5. Part 2:Designing ETL Pipelines with Python
6. Chapter 4: Sourcing Insightful Data and Data Extraction Strategies 7. Chapter 5: Data Cleansing and Transformation 8. Chapter 6: Loading Transformed Data 9. Chapter 7: Tutorial – Building an End-to-End ETL Pipeline in Python 10. Chapter 8: Powerful ETL Libraries and Tools in Python 11. Part 3:Creating ETL Pipelines in AWS
12. Chapter 9: A Primer on AWS Tools for ETL Processes 13. Chapter 10: Tutorial – Creating an ETL Pipeline in AWS 14. Chapter 11: Building Robust Deployment Pipelines in AWS 15. Part 4:Automating and Scaling ETL Pipelines
16. Chapter 12: Orchestration and Scaling in ETL Pipelines 17. Chapter 13: Testing Strategies for ETL Pipelines 18. Chapter 14: Best Practices for ETL Pipelines 19. Chapter 15: Use Cases and Further Reading 20. Index 21. Other Books You May Enjoy

A Primer on AWS Tools for ETL Processes

We believe that with the right tools and practices, extract, transform, and load (ETL) can be a powerful and streamlined process that enables organizations to leverage the full potential of their data assets. As we’ve moved through this book, we’ve started to shift the emphasis from the thought process and general structure of developing simple data pipelines to researching and leveraging various Python modules or Python-specific interfaces to create more powerful data pipelines. Within this chapter, we again expand on this idea of working directly with cloud-based interfaces that are language agnostic.

Amazon Web Services (AWS) is one of the most widely used platforms for company data integration systems. Its flexible pay scale and wide range of applications and resources enable this platform to be equally useful for both large-scale corporations and small-scale side projects at home. Entire books have been written about AWS...

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