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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

Product type Paperback

Published in Sep 2023

Publisher Packt

ISBN-13 9781804615256

Length 246 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Data Streaming

Authors (2):

Brij Kishore Pandey

Emily Ro Schoof

View More author details

Table of Contents (22) Chapters

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

Why subscribe?

21. Other Books You May Enjoy

References

The following are the references that we used in this chapter:

Reference 1: Nitin Anan, ETL and its impact on Business Intelligence, International Journal of Scientific and Research Publications, Volume 4, Issue 2, February 2014 1ISSN 2250-3153
https://www.academia.edu/11434594/ETL_and_its_impact_on_Business_Intelligence?email_work_card=title
Reference 2: Theodorou, Vasileios, et al. Frequent patterns in ETL workflows: An empirical approach. Data Knowl. Eng. 112 (2017): 1-16.
https://upcommons.upc.edu/bitstream/handle/2117/110172/DATAK_2016_201_Revision+1_V0.pdf;jsessionid=0B98AFF1323933 E58ABA42D6CC2C4304?sequence=4

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Authors (2)

Brij Kishore Pandey

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.

See other products by Brij Kishore Pandey

Emily Ro Schoof

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.

See other products by Emily Ro Schoof

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