dbt Medallion Build‑in‑Public

Akintola Malik Olasubomi has started a 14-day public build series that walks through a dbt project organized as bronze, silver, and gold data layers. (x.com) The setup uses Amazon Simple Storage Service, or Amazon S3, to land source data, Snowflake to store and process it, and Power BI to visualize the finished models. Olasubomi said the series is being built in public, day by day, rather than posted as a finished repo dump. (x.com) dbt, short for data build tool, is a framework for writing data transformations in SQL and managing them as software projects with tests, documentation, and lineage. dbt’s own introduction describes it as a workflow for transforming data in warehouses and lakehouses after it has already been loaded. (docs.getdbt.com) The medallion pattern splits that work into three layers: bronze for raw ingested data, silver for cleaned and standardized tables, and gold for business-ready models used in reporting. Databricks documents the pattern as a way to improve data quality step by step as data moves through the system. (docs.databricks.com) That makes the series less about one person’s demo stack and more about a common operating model for analytics teams. Companies that standardize on layered models usually need naming rules, testing habits, and deployment steps that analysts and engineers can repeat across projects. (docs.getdbt.com, docs.snowflake.com) Snowflake’s documentation now includes dbt project deployment, execution, scheduling with Snowflake tasks, and command-line hooks for continuous integration and continuous delivery workflows. Those are the same mechanics teams use when they move from notebook experiments to scheduled production jobs. (docs.snowflake.com) Power BI also has documented connectors for Snowflake and Amazon S3, which makes the stack legible to teams already using Microsoft dashboards on top of cloud data platforms. In practice, that means one example can cover ingestion, transformation, and reporting without switching to custom tooling at every step. (learn.microsoft.com, learn.microsoft.com) Build-in-public threads have become a common way for data practitioners to show project structure, tradeoffs, and deployment choices in small increments instead of long tutorials. Olasubomi’s format turns a layered analytics pattern into a dated sequence that other teams can inspect, copy, and adapt over the next two weeks. (x.com)