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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 1. Section 1 – Introduction, Concepts, and the Basics of AWS Glue
2. Chapter 1: Data Management – Introduction and Concepts 3. Chapter 2: Introduction to Important AWS Glue Features 4. Chapter 3: Data Ingestion 5. Section 2 – Data Preparation, Management, and Security
6. Chapter 4: Data Preparation 7. Chapter 5: Data Layouts 8. Chapter 6: Data Management 9. Chapter 7: Metadata Management 10. Chapter 8: Data Security 11. Chapter 9: Data Sharing 12. Chapter 10: Data Pipeline Management 13. Section 3 – Tuning, Monitoring, Data Lake Common Scenarios, and Interesting Edge Cases
14. Chapter 11: Monitoring 15. Chapter 12: Tuning, Debugging, and Troubleshooting 16. Chapter 13: Data Analysis 17. Chapter 14: Machine Learning Integration 18. Chapter 15: Architecting Data Lakes for Real-World Scenarios and Edge Cases 19. Other Books You May Enjoy

Analyzing usage

Due to the nature of a data platform, it is not practical to build it once and leave it as it is without any updates. This is because data volume, velocity, and variety increase day by day. Also, how the data is consumed and utilized can often vary. It is practical to build a platform based on the minimum requirement, start using it, measure end user activities, and continuously improve it based on end user feedback.

After you release the data platform to end users, you might see issues such as the following:

  • Less usage than expected
  • Less adoption in specific teams
  • Too many escalations from end users

To make the data platform useful for your end users, you need to maintain and keep improving the platform by tracking and analyzing end user activities.

Let’s look at how user activity can be measured for each type of activity. For example, if it is a simple data reference, it can be recorded and measured in the Amazon S3 server access...

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