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Serverless ETL and Analytics with AWS Glue

You're reading from   Serverless ETL and Analytics with AWS Glue Your comprehensive reference guide to learning about AWS Glue and its features

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

Preface 1. Section 1 – Introduction, Concepts, and the Basics of AWS Glue
2. Chapter 1: Data Management – Introduction and Concepts FREE CHAPTER 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

Glue’s integration with OpenSearch

Now, let’s focus on a search use case. Let’s say that you were interested in searching through log data. Amazon OpenSearch could be your answer to that. Originally, it was forked from Elasticsearch and comes with a visualization technology called OpenSearch Dashboards. OpenSearch Dashboards has been forked from Kibana. OpenSearch can work on petabytes of unstructured and semi-structured data. Additionally, it can auto-tune itself and use ML to detect anomalies in real time. Auto-Tune analyzes cluster performance over time and suggests optimizations based on your workload.

For the purpose of this chapter, we will use our employee data as the source and show how we can load the data into OpenSearch. Then, we will visualize the data in OpenSearch Dashboards.

The CloudFormation template creates a secret that stores the OpenSearch domain’s user ID and password. The Marketplace connection created by you using the OpenSearch...

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