Data Platform
Everything about building the systems that turn raw data into trustworthy, AI-ready data products. Two complementary guides: a zero-to-job curriculum that takes you from basic SQL and Python to job-ready, and a senior systems-design reference for architecting a real platform. Start with the curriculum if you're learning; jump to the reference if you're designing.
New to the field? Begin the curriculum at Becoming a Data Platform Engineer. Already experienced and designing a platform? Go straight to Data Platform Systems Design. The curriculum ladders up to that reference — and even has you build a laptop-scale version of it — so they're designed to be read together.
Guides
2 guidesA zero-to-job curriculum in eight courses — from CS foundations and the modern data stack through a runnable capstone build to the job search. Start with basic SQL and Python; finish able to design, build, and operate a real platform. Hands-on throughout, with checkpoints, exercises, a six-month schedule, a glossary, and a companion repo. Begins with the Roadmap, which maps all eight courses.
Data Platform Systems Design — GPU MarketplaceThe senior systems-design reference — how a real data platform is architected end to end (application space, ingestion, lakehouse storage, data modeling, transformation, serving & the semantic layer, governance, and AI enablement), reasoned through one GPU-marketplace case study. 17 chapters, three reference stacks compared (open-source lakehouse, Databricks, Snowflake), with many diagrams, tables, and a full reference design.