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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
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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

Best practices for data loading

There isn’t one universally definitive approach to creating data pipeline loading activities, but some methods are more effective than others. Proper preparation and adherence to best practices empower you to navigate the data loading phase with confidence, optimizing efficiency, accuracy, and reliability in your ETL workflow.

The process of designing a data loading activity reflects the level of understanding you have of the full environmental conditions of your system. You can use the following three principles to design a data loading workflow that is both scalable and reusable:

  • Utilizing techniques such as bulk loading, parallel processing, and optimized SQL queries can significantly enhance loading performance for large datasets. By adopting scalable strategies, you ensure that your data loading solution remains efficient and responsive even as data volume increases.
  • Automation streamlines the loading process, reducing the...
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