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Building ETL Pipelines with Python

You're reading from  Building ETL Pipelines with Python

Product type Book
Published in Sep 2023
Publisher Packt
ISBN-13 9781804615256
Pages 246 pages
Edition 1st Edition
Languages
Authors (2):
Brij Kishore Pandey Brij Kishore Pandey
Profile icon Brij Kishore Pandey
Emily Ro Schoof Emily Ro Schoof
Profile icon 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 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

Discussion – Building flexible applications in AWS

Before moving on to AWS applications used to help automate ETL development, we wanted to pause and discuss how to use AWS. S3 and EC2 instances are two core services offered by AWS that are frequently used together to build scalable and flexible applications in the cloud.

Leveraging S3 and EC2

Together, S3 and EC2 can be used in tandem to create a powerful and flexible platform for instantiating an application that is easily and reliably scalable in the cloud. S3 can be used as a storage backend for EC2 instances, where data can be stored and retrieved using S3 APIs, HTTP, or a CLI. By using S3 as the data source or target for ETL workflows, data can be ingested, processed, and stored in intermittent locations within different S3 buckets (such as staging and archive buckets). EC2 instances can then access the transformed output data from the S3 data directly over your network, without the need to copy or move data across...

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