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Serverless Analytics with Amazon Athena

You're reading from  Serverless Analytics with Amazon Athena

Product type Book
Published in Nov 2021
Publisher Packt
ISBN-13 9781800562349
Pages 438 pages
Edition 1st Edition
Languages
Authors (3):
Anthony Virtuoso Anthony Virtuoso
Profile icon Anthony Virtuoso
Mert Turkay Hocanin Mert Turkay Hocanin
Profile icon Mert Turkay Hocanin
Aaron Wishnick Aaron Wishnick
Profile icon Aaron Wishnick
View More author details
Toc

Table of Contents (20) Chapters close

Preface 1. Section 1: Fundamentals Of Amazon Athena
2. Chapter 1: Your First Query 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

Why isn't all data structured to begin with?

Data is generated from everywhere at all times within computer systems. They power our applications and reports and help us make sense of the world and our decisions that impact it. Data that's produced from an application that manages financial portfolios tells us how much risk the instruments in the portfolio are at. Websites can generate click data to tell a story, such as how customer's behavior changes when an update is made to a website. Retail businesses produce sales transactions and marketing data to determine how sales are affected by marketing campaigns. Amazon's user traffic information on individual products can train machine learning models to make recommendations to users who showcase products that they didn't even know they wanted. For this data to be helpful, it must be accessible to data engineers and machine scientists to produce even greater value from them.

However, not all data is created...

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