Book Image

Serverless Analytics with Amazon Athena

By : Anthony Virtuoso, Mert Turkay Hocanin, Aaron Wishnick
Book Image

Serverless Analytics with Amazon Athena

By: Anthony Virtuoso, Mert Turkay Hocanin, Aaron Wishnick

Overview of this book

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using SQL, without needing to manage any infrastructure. This book begins with an overview of the serverless analytics experience offered by Athena and teaches you how to build and tune an S3 Data Lake using Athena, including how to structure your tables using open-source file formats like Parquet. You’ll learn how to build, secure, and connect to a data lake with Athena and Lake Formation. Next, you’ll cover key tasks such as ad hoc data analysis, working with ETL pipelines, monitoring and alerting KPI breaches using CloudWatch Metrics, running customizable connectors with AWS Lambda, and more. Moving on, you’ll work through easy integrations, troubleshooting and tuning common Athena issues, and the most common reasons for query failure. You will also review tips to help diagnose and correct failing queries in your pursuit of operational excellence. Finally, you’ll explore advanced concepts such as Athena Query Federation and Athena ML to generate powerful insights without needing to touch a single server. By the end of this book, you’ll be able to build and use a data lake with Amazon Athena to add data-driven features to your app and perform the kind of ad hoc data analysis that often precedes many of today’s ML modeling exercises.
Table of Contents (20 chapters)
Section 1: Fundamentals Of Amazon Athena
Section 2: Building and Connecting to Your Data Lake
Section 3: Using Amazon Athena
Chapter 11: Operational Excellence – Monitoring, Optimization, and Troubleshooting
Section 4: Advanced Topics


In this chapter, you learned about common usages of the ETL pattern, including integration, aggregation, modularization, and performance. The integration patterns offer a lowest-common-denominator approach to connecting disparate systems, even if they have no native support for integrating with each other. ETL for aggregations helps produce a single source of truth (SSOT) for getting a view of data across your estate. This is a common pattern for creating data lakes that work with services such as Athena. Modularization is an approach for using ETL to break up monolithic processes that are difficult to maintain or operationally prone to failure. Lastly, ETL for performance is a technique that moves expensive or time-consuming processing out of the live query path by either creating materialized views or running other pre-computations of anticipated workloads.

Armed with this knowledge of ETL design patterns, you reviewed key criteria for designing ETL queries for use with...