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

Discovering your datasets on S3 using AWS Glue Crawlers

Let's say that you have a lot of data that you are outputting to S3, and you want to query it. Before you can, you need to register that data. However, the data sitting in S3 is in many different formats and schemas. Going through each dataset, inspecting files, and determining the file format, partitions, and columns is a very time-consuming task. If a table contains incorrect column names, incorrect ordering of columns, or any other form of error, then the table may not be queryable until it is corrected. AWS Glue Crawlers solve these issues. Glue Crawlers can scan data on S3, inspect the S3 directory structure and data within it, and automatically populate the data catalog. This section will look at how they work and set up a Glue crawler to discover a sample dataset.

How do AWS Glue Crawlers work?

There are three actions that a Glue crawler takes when scanning S3:

  1. It scans S3 directories for data files...