AWS Glue, S3, and Athena — a beginner’s real-world workflow

2026-06-22 · Source: Data Engineering on Medium · Field: Technology & Digital — Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Novice, short

Summary

This article outlines a practical, serverless data pipeline workflow utilizing AWS S3, Glue, and Athena, designed for beginners. It clarifies how S3 serves as cloud storage for raw files like CSVs and JSONs, Glue Crawlers infer data schemas and populate the Glue Data Catalog, and Athena enables direct SQL querying of S3 data without managing a database server. The workflow extends to using Glue ETL jobs for data transformation, cleaning, and writing processed data back to S3. Key recommendations include organizing S3 files by date or category, converting data to Parquet format, and partitioning data to optimize Athena query costs by up to 80%. The author also highlights common beginner mistakes, such as storing everything as CSV, not partitioning data, running crawlers too often, and ignoring IAM permissions.

Key takeaway

For data analysts or engineers building cloud data pipelines, understanding the integrated workflow of AWS S3, Glue, and Athena is crucial. You should prioritize data partitioning and Parquet conversion from the outset to significantly reduce Athena query costs and improve performance. Implement IAM permissions early to avoid debugging access errors. This serverless approach allows you to scale from small datasets to terabytes using familiar SQL, minimizing infrastructure management overhead.

Key insights

AWS S3, Glue, and Athena form a cohesive serverless pipeline for storing, understanding, and querying cloud data efficiently.

Principles

• S3 stores it, Glue understands it, Athena questions it.
• Partitioning data optimizes query performance and cost.
• Convert to Parquet for efficient cloud data processing.

Method

Set up an S3 bucket, run a Glue crawler to infer schema, query with Athena using SQL, then use Glue ETL for transformations and writing clean data back to S3.

In practice

• Organize S3 files by date/category.
• Use `boto3` for S3 file uploads.
• Set IAM roles correctly from day one.

Topics

• AWS S3
• AWS Glue
• AWS Athena
• Serverless Data Pipelines
• Data Partitioning
• ETL

Best for: Data Analyst, Data Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.