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Masterclass

AWS data lake masterclass

Two focused courses on the AWS data lake stack: PySpark on Glue, then Iceberg plus Snowflake plus Airflow plus Terraform.

Start this masterclass

Still deciding? Ask first.

Message a mentor about fit, prerequisites, or where to start. Replies come on WhatsApp, usually within a day.

  • Curriculum fit, prerequisites, or where to start
  • Honest answer, no pressure to enroll

Engineers are learning here from

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Outcome

What you'll be able to do.

Operate an AWS lakehouse end to end. Defend the architecture in a review. Debug a failed Glue write. Roll back a bad deploy with git revert.

  • Write a PySpark transform that runs identically locally and on AWS Glue 4.0
  • Ship Glue jobs through CodeBuild on every push, with idempotent create-or-update
  • Build the lakehouse: Lambda extract to S3, Glue PySpark to Apache Iceberg
  • Wire Snowflake Catalog Integration plus External Volume to read Iceberg without copying
  • Orchestrate the daily refresh with Airflow, Lambda fan-out, and Snowflake refresh
  • Provision S3, IAM, Glue, CodeBuild, and the Snowflake-AWS trust policy with Terraform

Projects you build

Portfolio pieces you can demo.

Each project ships as real code you run locally, not slides you watch. Walk into your next review with something on screen.

01Project

A PySpark transform that ships through CodeBuild

Take a 336K-row flight dataset from a raw CSV in S3 to a curated Parquet warehouse, partitioned by year and carrier. Same code runs locally and inside Glue.

PySparkAWS GlueAWS S3AWS CodeBuildParquet

You ship: A Glue job that redeploys on every push and a local iteration loop that costs zero DPU-seconds.

02Project

A working AWS lakehouse with Iceberg and Snowflake

Lambda extracts a weather API to S3, Glue PySpark writes Apache Iceberg tables in the Glue Data Catalog, Snowflake reads them as external Iceberg tables. Airflow orchestrates, Terraform provisions.

AWS LambdaApache IcebergAWS GlueSnowflakeAirflowTerraform

You ship: A lakehouse where Glue writes once and Snowflake reads without copying. Schema evolution is a metadata commit.

Curriculum

What's inside.

  1. 01

    AWS Glue and PySpark ETL on a real flight dataset

    Write a PySpark transform that runs identically locally and on AWS Glue 4.0. Ship via CodeBuild, validate with a local smoke run, and skip the surprise DPU bills.

  2. 02

    AWS lakehouse with Apache Iceberg, Glue, and Snowflake

    Lambda to S3 to Glue PySpark to Iceberg in the Glue Data Catalog, queried by Snowflake as external tables. Airflow orchestrates. Terraform provisions. CodeBuild ships.

Who it's for

Is this for you?

Data engineers

tasked with building a "lakehouse" without a clear story for how Snowflake and Glue cooperate without doubling storage cost

Analytics engineers

who already use Snowflake and want their dbt models to read straight from raw Iceberg without nightly copy jobs

Backend engineers

inheriting a Glue job they cannot debug locally because the Glue runtime is opaque

Platform engineers

wiring Iceberg for the first time and discovering Glue, Snowflake, IAM, and Catalog Integration each have surprising defaults

What you'll earn

Ship it, earn it.

Glue ready

Complete the AWS Glue + PySpark course

Lakehouse architect

Build the Iceberg + Snowflake stack

AWS data engineer

Complete the AWS data lake masterclass

Pricing

Pick the path that fits.

Self-paced forever, or mentor-led when you want live feedback.

FAQ

Frequently Asked Questions

Do I need a real AWS or Snowflake account?
No. Both courses run a green smoke test locally with no paid credentials. The deployment chapters show the AWS Console and Snowflake worksheets so when you do deploy, the path is mechanical.
Why Iceberg instead of plain Parquet?
Plain Parquet is files in a folder. Iceberg adds a manifest layer that tracks snapshots, schema evolution, and partition pruning. Two engines reading the same Iceberg tables see consistent data even during writes.
Can I use Databricks instead of Glue?
Yes. The Iceberg layer is identical. The Glue PySpark job swaps for a Databricks notebook or job. The Snowflake side does not change.
How does this differ from the Data Engineering Bootcamp?
The bootcamp covers the full stack from medallion notebook to enterprise platform across the whole data engineering surface. This masterclass focuses purely on the AWS lakehouse pattern, deeper and shorter.

Pick your next step.

Two focused courses on the AWS data lake stack: PySpark on Glue, then Iceberg plus Snowflake plus Airflow plus Terraform.

Start this masterclass

AWS data lake masterclass

Self-paced masterclass

Enroll