Our organization hosts its data lake on Amazon S3 and we run analytical queries using Athena. We want to build executive visual reports. What are the best practices to connect sql queries to data visualization tools within the AWS ecosystem, specifically mapping Athena to QuickSight? We want to keep cloud computing costs low while maintaining fast dashboard rendering times for our leadership team.
3 answers
Connecting Athena queries to Amazon QuickSight is incredibly straightforward since both tools live inside the AWS environment. In QuickSight, create a new dataset and select AWS Athena as your source. Enter your target database names, and then choose 'Use custom SQL' to input your query. To optimize your cloud costs and maximize performance, you must avoid querying your raw S3 files directly every time an executive opens a dashboard. Instead, import your Athena query results straight into QuickSight’s SPICE in-memory engine, and schedule it to refresh once a day.
Does using the SPICE engine incur significant extra storage costs within AWS, or is it economical compared to running active Athena scans on every single dashboard click?
Ensure that your underlying S3 data files are saved in a columnar format like Apache Parquet, which allows Athena queries to execute much faster and scan less data overall.
Converting CSV files to Parquet format is a massive game-changer, Aaron. It optimizes the data structure perfectly, cutting down cloud infrastructure expenses and boosting report execution speeds at the same time.
SPICE is actually incredibly cost-effective, Bradley. Athena charges you based on the total terabytes of data scanned per query. If your executives look at dashboards frequently, active Athena scans will rapidly inflate your monthly bill. SPICE charges a small flat rate for data storage, making it significantly cheaper.