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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

Table of Contents (20) Chapters

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

Chapter 2: Introduction to Amazon Athena

The previous chapter walked you through your first, hands-on experience with serverless analytics using Amazon Athena. This chapter will continue that introduction by discussing Athena's capabilities, scalability, and pricing in more detail. In the past, vendors such as Oracle and Microsoft produced mostly one-size-fits-all analytics engines and RDBMSes. Bucking the historical norms, AWS has championed a fit for purpose database and analytics strategy. By optimizing for specific use cases, the analytics engines' very architecture could exploit nuances of the workload for which they were intended, thereby delivering an all-around better product. For example, Redshift, EMR, Glue, Athena, and Timestream all offer related but differentiated capabilities with their own unique advantages and trade-offs. The knowledge you will gain in this chapter provides a broad-based understanding of what functionality Athena offers as well as a set of...

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