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Serverless Analytics with Amazon Athena

You're reading from   Serverless Analytics with Amazon Athena Query structured, unstructured, or semi-structured data in seconds without setting up any infrastructure

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Product type Paperback
Published in Nov 2021
Publisher Packt
ISBN-13 9781800562349
Length 438 pages
Edition 1st Edition
Languages
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Authors (3):
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Aaron Wishnick Aaron Wishnick
Author Profile Icon Aaron Wishnick
Aaron Wishnick
Mert Turkay Hocanin Mert Turkay Hocanin
Author Profile Icon Mert Turkay Hocanin
Mert Turkay Hocanin
Anthony Virtuoso Anthony Virtuoso
Author Profile Icon Anthony Virtuoso
Anthony Virtuoso
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Fundamentals Of Amazon Athena
2. Chapter 1: Your First Query FREE CHAPTER 3. Chapter 2: Introduction to Amazon Athena 4. Chapter 3: Key Features, Query Types, and Functions 5. Section 2: Building and Connecting to Your Data Lake
6. Chapter 4: Metastores, Data Sources, and Data Lakes 7. Chapter 5: Securing Your Data 8. Chapter 6: AWS Glue and AWS Lake Formation 9. Section 3: Using Amazon Athena
10. Chapter 7: Ad Hoc Analytics 11. Chapter 8: Querying Unstructured and Semi-Structured Data 12. Chapter 9: Serverless ETL Pipelines 13. Chapter 10: Building Applications with Amazon Athena 14. Chapter 11: Operational Excellence – Monitoring, Optimization, and Troubleshooting 15. Section 4: Advanced Topics
16. Chapter 12: Athena Query Federation 17. Chapter 13: Athena UDFs and ML 18. Chapter 14: Lake Formation – Advanced Topics 19. Other Books You May Enjoy

Understanding the uses of ETL

In the most literal terms, ETL refers to a procedure with three conceptual phases that begin with reading data from a source system and end with a derivative of the original data being stored into a target system. In between these deceptively simple steps sits the most important facet of ETL, the transformation from the source system's semantic and physical schema to the domain model expected by the target system. In this step, we are essentially integrating source and target systems that may represent data differently.

Much of the academic literature on ETL points to the expansion of data warehousing concepts in the 1970s as its origin. It was a time when businesses rapidly adopted databases and found themselves with multiple data repositories, often using incompatible formats. Sounds familiar? Fast forward to today, and not much has changed aside from the date. The ability to integrate data from siloed or incompatible systems continues to be...

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