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 ETL queries

While this book's goal is not to teach Structured Query Language (SQL), it is beneficial to spend some time reviewing everyday SQL recipes and how they relate to Athena's strengths and quirks. Transforming data from one format to another, producing intermediate datasets, or simply running a query that outputs many megabytes (MB) or gigabytes (GB) of output necessitates some understanding of Athena's best practices to achieve peak price/performance. As we did in Chapter 1, Your First Query, let's start by preparing a larger dataset for our exercises.

We will continue using the NYC Yellow Taxi dataset, but we will prepare 2.5 years of this data this time. Preparing this expanded dataset will entail downloading, compressing, and then uploading dozens of files to S3. To expedite that process, you can use the following script to automate the steps. To do so, add all the files from yellow_tripdata_2018-01.csv through yellow_tripdata_2020-06.csv...