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

Running approximate queries

In Chapter 1, Your First Query, we used TABLESAMPLE to run a query that allowed us to get familiar with our data by viewing an evenly distributed sampling of rows from across the entire table. TABLESAMPLE enables you to approximate the results of any query by sampling the underlying data. Athena also supports more targeted forms of approximation that offer bounded error. For example, the approx_distinct function should produce results with a standard error of 2.3% but completes its execution 97% faster while also using less peak memory than its completely accurate counterpart, COUNT(DISTINCT x). We'll learn more about these and several other approximate query tools by exploring our NYC taxi ride tables.

TABLESAMPLE is a somewhat generic technique for running approximate queries. Unlike the other methods we discuss in this section, TABLESAMPLE works by sampling the input data. This allows you to use it in conjunction with any other SQL features supported...