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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

Accessibility to data

Even though there are many existing data sources, some of them might be inaccessible, unreliable, or irrelevant to the purpose of your pipeline. Further, measuring the accessibility of data sources is dependent on circumstances that are oftentimes outside of your direct control, such as procuring the right access credentials, assessing the finesse of a company’s cybersecurity system, or simply having a limitation in your system’s short-term storage capacity.

Whatever the unique limitations of data accessibility are, it needs to be accounted for within the design of your architecture since the ability to connect to data sources with consistency has heavy impacts on downstream workflows. Being aware of the frequency of connection for each data source is essential to not only choose the most accessible data source but also to help determine the cadence of certain analytics due to the restriction of how often various sources can be merged together...

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