Athena & Glue: Query a Data Lake
Crawl S3 data with AWS Glue, query it with Athena, and see how partition pruning cuts cost and latency in a serverless data lake.

Lab Overview
Build a serverless data lake pattern end-to-end. You will upload partitioned CSV sales data to S3, create a Glue crawler that automatically discovers the schema and populates the Data Catalog, then query the data interactively with Athena. Finally, you will compare a full-table scan against a partition-pruned query to see the cost and performance difference first-hand.
You'll learn to:
- Organize S3 data using Hive-style partitioning (year=YYYY/month=MM)
- Create an AWS Glue IAM role, database, and crawler
- Run a Glue crawler and inspect the resulting Data Catalog table
- Execute SQL queries with Amazon Athena from the CLI
- Use partition filters to reduce data scanned and lower query cost
What You'll Learn
Upload partitioned data to S3 using Hive-style key prefixes
Create an AWS Glue database, IAM role, and crawler
Run a Glue crawler and verify the Data Catalog table
Query data with Athena SQL from the AWS CLI
Apply partition pruning to reduce data scanned and cost
Prerequisites
AWS CLI basics
S3 bucket fundamentals
Basic SQL knowledge
Technologies Covered
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