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Run a real production data platform end to end: batch, streaming, warehouse, ML tracking, lineage, and observability, all fronted by a typed FastAPI control plane.
Message a mentor about fit, prerequisites, or where to start. Replies come on WhatsApp, usually within a day.
Engineers are learning here from
Operate a production-grade twenty-service data platform: Airflow orchestrates batch, Kafka carries streams, Spark transforms both, Postgres and Snowflake serve the warehouse, MLflow tracks models, Atlas captures lineage, Prometheus and Grafana observe everything, and a Python FastAPI control plane ported 1:1 from a .NET 8 reference sits in front of it all. The top tier of the learnwithparam data engineering track.
Run a twenty-service production data platform end to end: batch, streaming, warehouse, ML tracking, lineage, observability, and a typed FastAPI control plane.
What you'll ship
What you'll learn
Curriculum
The enterprise data platform topology
Understand why a production data platform needs twenty services, which ones carry real load, and which ones are optional extensions
Governance, ML, and observability
Register lineage with Atlas, track experiments with MLflow, and render Grafana dashboards that predict real incidents
Control plane, the .NET port, and Kubernetes deploy
Port a .NET 8 FastAPI backend 1:1 to Python, preserve HTTP contracts, and deploy the stack to Kubernetes with Helm and Terraform
Who it's for
moving into a data platform role and needing the full enterprise toolchain without months of trial and error
owning a real platform and wanting a reference implementation they can point a team at
asked to run the data platform and wanting a teachable shape that matches what tools like Databricks and Snowflake replace
tired of running experiments outside the data platform and wanting MLflow wired into real Airflow tasks
FAQ
No. Everything runs on docker-compose. The course shows the Helm charts and Terraform stubs so you know what the production port looks like, and you can point them at a managed Kubernetes cluster on your own time.
Because a lot of enterprise data platforms have a .NET 8 backend you will eventually be asked to modernize. The 1:1 port exercise teaches you how to preserve HTTP contracts and options-class config while swapping the runtime. The companion repo ships both shapes so you can compare.
You will touch enough to understand the shape. Kafka, Snowflake, MLflow, Atlas, Prometheus, Grafana, and the FastAPI layer are covered directly. MongoDB, Redis, InfluxDB, and Elasticsearch are optional upstream consumers the control plane can health-check but does not depend on. The course points at each so you can extend.
Yes. Databricks and Snowflake are managed platforms that replace some of what you build here. Learning the unbundled shape first makes you a better operator of those managed platforms, because you understand what they hide.
Pricing
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Enterprise data platform from ingestion to governance
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