How to Query Shredded Snowplow Events Using AWS Athena: A Comprehensive Guide

AWS Athena is a serverless query service that enables users to run SQL queries directly on data stored in S3, making it an attractive and cost-effective solution for Snowplow users managing large volumes of data with variable query workloads. While traditionally used for querying 'bad' data, Athena offers significant advantages for querying 'good' Snowplow data by eliminating the need for a permanent Redshift cluster and allowing cost-effective data exploration, particularly with shredded data stored as new-line delimited JSON files in S3. The process of querying shredded data involves creating Data Definition Language (DDL) tables in Athena for each shredded table, with considerations for partitioning to optimize query efficiency and costs. Challenges such as partitioning issues and data filtering can lead to higher query costs if not managed properly, necessitating practices like data cleanup and schema-specific querying to improve efficiency. Athena's ability to extract specific fields from JSON structures and transform data to match Redshift's atomic events table enhances its utility, though users must navigate potential challenges in partitioning and join operations. By optimizing partitioning strategies and staying informed on updates from Snowplow, users can leverage Athena to achieve efficient, scalable, and cost-effective querying of shredded Snowplow data.