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Part of the bootcamp
Data engineering foundations with the medallion pattern is one workshop inside Data engineering bootcamp.
See the full path with mentor reviews and a portfolio outcome.
Stop stitching pandas scripts. Rehearse the bronze, silver, gold pattern on a real dataset in DuckDB and pandas before you touch Airflow, Spark, or a cloud warehouse.
Engineers are learning here from
Take a real dataset from raw dump to BI-ready marts using the bronze, silver, gold pattern. All in DuckDB and pandas, all in a single notebook, no cluster or orchestrator required. The foundation every data engineer needs before touching Airflow or Spark.
Bronze, silver, gold warehouse patterns with DuckDB and pandas, rehearsed on a real dataset before you touch Airflow or Spark.
What you'll ship
What you'll learn
Curriculum
The medallion pattern and why it exists
See why stitching pandas scripts fails at the first hard question, and frame the bronze, silver, gold contract you will build in the rest of the course
Build bronze and silver with DuckDB
Ingest a real dataset into a bronze table without mutating a byte, then clean it into a silver layer a human can trust on sight
Gold star schema, marts, and charts
Model silver into a dimensional schema with surrogate keys, slice it into marts, and render charts so BI and engineering agree on the numbers
Who it's for
dragged into "fix the data pipeline" when the pipeline is actually a folder of unmarked scripts
tired of re-running the same pandas chain every week because nothing is reusable
trying to build RAG or features on top of data that was never modeled properly
who inherit tribal knowledge and want a pattern that survives the next person leaving
FAQ
No. The point is to rehearse the pattern on your laptop with DuckDB and pandas before you pay for a cluster. The same pattern scales to Spark, Snowflake, or BigQuery later. The next course in the track shows that jump.
dbt is one tool that happens to implement medallion-style staging and marts. The pattern is older and tool-agnostic. Once you understand the layered contract, dbt, DuckDB, Spark, and Snowflake all become implementation details.
Pandas gives you dataframes. DuckDB gives you SQL on top of those dataframes. Real warehouses speak SQL, so rehearsing on DuckDB means your gold layer code transfers when you move to Snowflake or Postgres without rewriting.
Yes. The layered model, star schema, surrogate keys, and quality gates are standard interview territory. The notebook is the practical side of what the interviewer is probing when they ask "how would you model this?".