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

Querying JSON data

JSON is a prevalent data format. It can be described as a lightweight version of XML and has many similarities with it. The file format is text-based, contains field names, along with their values, and supports advanced data types such as structures and arrays. A structured data type is a group of columns that are stored and referred to by their structure names and column names. This allows for logically similar columns to be grouped; for example, the structure of a customer's address that contains a street name, street number, city, state, and more. Arrays allow a single row to have a field containing zero or more values that can be referenced by an index number. An example list would be a list of addresses for a customer. JSON supports a mixture of arrays and structures. You can have an array of structures or a structure with an array field within it.

When using Athena, JSON files have to be of a particular format. Athena requires that JSON files must...