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)
1
Section 1: Fundamentals Of Amazon Athena
5
Section 2: Building and Connecting to Your Data Lake
9
Section 3: Using Amazon Athena
14
Chapter 11: Operational Excellence – Monitoring, Optimization, and Troubleshooting
15
Section 4: Advanced Topics

Designing ETL queries for Athena

This section highlights workload traits and design considerations that Athena customers sometimes overlook creating ETL pipelines. Many of the items we are about to discuss are not specific to Athena. We'll be sure to note the ones that do stem from idiosyncrasies in the way Athena works. Generally speaking, there are no differences between regular Athena queries and those intended for use in an ETL pipeline. All of the performance suggestions covered in Chapter 2, Introduction to Amazon Athena, apply, and all the same Athena features are applicable across ad hoc analytics, ETL, and other use cases.

Don't forget about performance

Since ETL is not expected to be an interactive process, it allows us to run more time-consuming operations than we might otherwise. Just because ETL is typically viewed as an offline or asynchronous process that doesn't have a human sitting at a screen waiting for a response doesn't mean you can ignore...