Data Platform
Everything about building the systems that turn raw data into trustworthy, AI-ready data products — a zero-to-job curriculum, senior systems-design references for the platform itself and for the ML data plane on top of it, and interview prep for the roles that own them. Start with the curriculum if you're learning; jump to a reference if you're designing.
11 guides·108 chapters·~14 h of reading
No.GuideFormatLengthRead
Curriculum8
- 01Capstone LabsEverything comes together. Across ten labs you'll build mini-GridDP — a complete, running data platform for a GPU-rental marketplace, on your laptop: a data generator, ingestion, a modeled warehouse, a telemetry rollup, orchestration, tests, a semantic layer, a dashboard, CI/CD, and documentation. Each lab maps to a chapter of the senior Systems Design reference, so you build the very system that guide designs — at laptop scale.Curriculum11 ch.~1 h
- 02Career & Getting the JobYou've built the skills and the capstone — now turn them into an offer. This course covers the whole job-search machine: how the market and roles actually work, how to package your capstone into a portfolio that gets callbacks, a resume that survives the screen, structured interview prep for every round (SQL, coding, system design, behavioral), and how to thrive once you land — negotiation and the first 90 days.Curriculum7 ch.~1 h
- 03FoundationsThe computer-science and data-systems core — taught for this job. This is the course that turns you from someone who can use tools into someone who understands why they work, so you can reason about new ones, debug the weird failures, and design rather than copy. It's the degree-equivalent material, pruned to what a data platform engineer actually uses, and grounded in concrete examples throughout.Curriculum12 ch.~2 h
- 04Glossary & Cheat SheetsThe reference to keep open the whole way through. A plain-language glossary of every term the curriculum uses — each cross-linked to the course that teaches it — plus quick cheat sheets for SQL, the command line, the data stack, and the decisions you'll make most often. When a word is fuzzy, this is where you look.Curriculum3 ch.~1 h
- 05Orientation & SetupThe first hands-on course. We install the mental model that makes everything else make sense, then build the working environment every data engineer lives in — the command line, git, a real Python toolchain, and Docker running a local Postgres and DuckDB — and finish by running your first end-to-end data pipeline. From here on, you learn by doing.Curriculum7 ch.~1 h
- 06Start Here & RoadmapBefore you learn anything technical, get the map. This short course explains what a Data Platform Engineer actually does, lays out the eight-course curriculum and how the pieces ladder together, gives you a realistic six-month schedule, and shows you how to study so the effort sticks. Start here — it will save you weeks.Curriculum5 ch.~1 h
- 07The DE CraftNow you build. Foundations explained how data systems work; this course turns that understanding into the daily craft of a data engineer — modeling data so it's trustworthy, moving it with ingestion and change-data-capture, transforming it with dbt, storing it in a lakehouse, handling streams, scheduling it all with an orchestrator, and proving it's correct with tests. Every chapter is hands-on and builds on your local stack from Course 2.Curriculum12 ch.~2 h
- 08Tooling & the Modern StackYou can build the parts; now learn to ship them like a professional. This course is about the engineering workflow around the data work — collaborating with git, containerizing and wiring your whole stack with Docker, the cloud primitives every platform runs on, automating tests and deploys with CI/CD, defining infrastructure as code, and monitoring it all. This is the difference between "it works on my machine" and "it works for the team, in production."Curriculum8 ch.~1 h
Deep dive2
- 09Data Platform Systems Design — GPU MarketplaceHow a Data Platform Engineer designs the internal data platform for a GPU-marketplace company, end to end — from the application space and ingestion through lakehouse storage, data modeling, transformation, serving, governance, and AI enablement. Layered for both learners and senior architects. Built around GridDP, an illustrative platform for a hypothetical GPU marketplace, and three reference stacks compared throughout: open-source lakehouse, Databricks, and Snowflake.Deep dive17 ch.~2 h
- 10ML Data ArchitectureThe ML data plane end to end — training-data pipelines with point-in-time correctness, labels and feedback loops, the feature store, storage and dataset versioning, streaming features, embeddings, serving, lineage, quality and drift, governance, cost, and three reference architectures — built on the GridDP case.Deep dive15 ch.~2 h
Interview prep1