AWS Workshop - Build AWS Data Lake

60-minute walkthrough of how engineering teams build production data pipelines on AWS in a single day. We cover architecture, live demo, and what your team needs to run this independently.

Full lab + screenshots: https://becloudready.com/workshops

WHAT YOU'LL WATCH US BUILD, LIVE

• A raw CSV (Kaggle Crude Oil price data, ~6,400 rows) land in S3
• A Glue Crawler infer the schema and register a table in the Glue Catalog
• An Athena SQL query run against that CSV — count, filter, aggregate, with zero servers to manage
• A PySpark Glue ETL job turn the CSV into partitioned Parquet — columnar, compressed, about 10x cheaper to scan
• A second crawler register the Parquet table
• The same query run again — with a side-by-side look at "data scanned": CSV vs Parquet

That last part is the punchline. At scale, Parquet vs CSV is the difference between a $5 query and a 50-cent query. Seeing it live in the Athena UI hits different than reading about it.

WHAT YOU'LL LEAVE WITH

• The full lab, open-sourced. Terraform for the IAM, sandbox, Glue, and Athena setup. The PySpark ETL script. A step-by-step walkthrough you can re-run on your own AWS account.
• A clear mental model of every data lake project: land raw, catalog, transform, catalog again, query. The dataset is just the variable.
• IAM patterns most tutorials skip — region-locking, prefix-scoped S3 access, Glue role policies that don't accidentally grant access to the world. The stuff that shows up in real production reviews.
• A take-home assignment: run the same pipeline on a Kaggle dataset of your choice, and bring it to the next session for feedback.
• Full lab instructions and screenshots: https://becloudready.com/learn/roadmaps/aws-data-engineer

WHO THIS IS FOR

• Data engineers who've shipped parts of a data lake but want to see the full pipeline end-to-end
• Career changers moving into data engineering — this is a portfolio-grade project you can talk about in interviews
• Bootcamp grads and self-taught engineers who know SQL and Python but haven't seen how Glue, Athena, and S3 connect
• Backend engineers picking up data work who want a fast on-ramp to the AWS data stack
• Anyone whose manager said "we should build a data lake" and now it's on your plate

Never opened the AWS console? You'll still follow along — we explain every click. Been doing this for years? You'll still pick up the IAM scoping pattern.

FORMAT

• Live on Microsoft Teams — full screen-share, no slides
• 60 minutes — same length as the lab itself
• Recording shared with everyone who registers
• Open Q&A throughout, not just at the end

ABOUT YOUR HOST

Chandan Kumar — founder of beCloudReady (https://www.becloudready.com) and organizer of TorontoAI (https://www.toronto-ai.org), a 10,000+ member community of AI and data builders. Twenty-plus years across software, cloud, and data engineering. Maintainer of open-source labs and the db-agent project, presented at AAAI-25 (https://github.com/db-agent/db-agent).
Full hands-on lab, with screenshots and instructions: https://becloudready.com/learn/roadmaps/aws-data-engineer