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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

Product type Paperback

Published in Nov 2021

Publisher Packt

ISBN-13 9781800562349

Length 438 pages

Edition 1st Edition

Languages

Python

Tools

Amazon Athena

Concepts

Data Processing

Authors (3):

Aaron Wishnick

Mert Turkay Hocanin

Anthony Virtuoso

View More author details

Table of Contents (20) Chapters

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

Technical requirements

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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Authors (3)

Virtuoso

Virtuoso

Anthony Virtuoso works as a Principal Engineer at Amazon and holds multiple patents in distributed systems, software defined networks, and security. In his eight years at Amazon, he has helped launch several Amazon Web Services, the most recent of which was Amazon Managed Blockchain. As one of the original authors of Athena Query Federation, you'll often find him lurking on the Athena Federation GitHub repository answering questions and shipping bug fixes. When not at work, Anthony obsesses over a different set of customers, namely his wife and two little boys, aged 2 and 5. His kids enjoy doing science experiments with dad, like 3D printing toys, building with Lego, or searching the local pond for tardigrades.

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Wishnick

Wishnick

Aaron Wishnick works as a Senior Software Engineer at Amazon, where he has been for 7 years. During that time he has worked on Amazon's payment systems, financial intelligence systems, as well as working for AWS on Athena and AWS Proton. When not at work, Aaron and his fiance, Alyssa, are on a quest to determine just how much dog fur is too much, with their husky and malamute, Mina and Wally.

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Mert Turkay Hocanin

Mert Turkay Hocanin

Mert Turkay Hocanin is a Principal Big Data Architect at Amazon Web Services within the AWS Glue and AWS Lake Formation services and has previously worked for several other services including Amazon Athena, Amazon EMR, Amazon Managed Blockchain. During his time at AWS, he worked with several Fortune 500 companies on some of the largest data lakes in the world and was involved with the launching of three Amazon Web Services. Prior to being a Big Data Architect, he was a Senior Software Developer within Amazon's retail systems organization building one of the earliest data lakes in the company in 2013. When he is not helping customers build data lakes, he enjoys spending time with his wife-Subrina, son-Tristan, and exploring New York City.

See other products by Mert Turkay Hocanin

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