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Serverless ETL and Analytics with AWS Glue

You're reading from  Serverless ETL and Analytics with AWS Glue

Product type Book
Published in Aug 2022
Publisher Packt
ISBN-13 9781800564985
Pages 434 pages
Edition 1st Edition
Languages
Authors (6):
Vishal Pathak Vishal Pathak
Profile icon Vishal Pathak
Subramanya Vajiraya Subramanya Vajiraya
Profile icon Subramanya Vajiraya
Noritaka Sekiyama Noritaka Sekiyama
Profile icon Noritaka Sekiyama
Tomohiro Tanaka Tomohiro Tanaka
Profile icon Tomohiro Tanaka
Albert Quiroga Albert Quiroga
Profile icon Albert Quiroga
Ishan Gaur Ishan Gaur
Profile icon Ishan Gaur
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Table of Contents (20) Chapters

Preface Section 1 – Introduction, Concepts, and the Basics of AWS Glue
Chapter 1: Data Management – Introduction and Concepts Chapter 2: Introduction to Important AWS Glue Features Chapter 3: Data Ingestion Section 2 – Data Preparation, Management, and Security
Chapter 4: Data Preparation Chapter 5: Data Layouts Chapter 6: Data Management Chapter 7: Metadata Management Chapter 8: Data Security Chapter 9: Data Sharing Chapter 10: Data Pipeline Management Section 3 – Tuning, Monitoring, Data Lake Common Scenarios, and Interesting Edge Cases
Chapter 11: Monitoring Chapter 12: Tuning, Debugging, and Troubleshooting Chapter 13: Data Analysis Chapter 14: Machine Learning Integration Chapter 15: Architecting Data Lakes for Real-World Scenarios and Edge Cases Other Books You May Enjoy

Partition management

In the previous sections, we discussed how to automatically update and add partitions to tables. This means that with an easy setup, Glue is capable of adding partitions continuously as your dataset grows.

For very large data lakes, however, this setup can easily run into issues. Glue supports up to 10 million partitions per table by default; however, having such a large number of partitions will increasingly lower your query execution times without proper management.

Partition indexes

Let’s take the example of a table storing product sales information. The table is partitioned by product category, and even though the business started small and we had only a handful of categories, as we expanded and added external sellers, we are now in the tens of thousands of categories.

Our business analysts want to query data based on product families, and so their Glue ETL queries usually include a WHERE CATEGORY= clause, filtering by category. Every time...

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