Building a Governed Snowflake and Power BI Analytics Layer

Executive perspective

Raw market data from different publishers can mix units, currencies, reporting periods and definitions. Mineral commodity analysis especially needs explicit source lineage and supply-concentration calculations to avoid misleading geographic comparisons.

Engagement and solution architecture

The engagement repository includes Snowflake SQL structures for raw, core and reporting layers; Power BI integration guidance; and an HTML analytical dashboard covering lithium, graphite, manganese and cobalt across price, production and supply-concentration questions.

Data model and analytical method

Ingest source extracts unchanged into raw tables with timestamps. In core, standardize country, commodity, unit and date identifiers while retaining source provenance. Publish curated reporting facts at consistent grain. Calculate concentration and trend metrics only after resolving missing data and denominators.

What business teams can investigate

Market-price trends support directional comparison, production views examine capacity and output distribution, and supply concentration highlights geographic reliance and risk scenarios. Power BI presents a governed semantic layer rather than business logic replicated across charts.

Governance and measurement integrity

Treat representative company names and illustrative numbers in the repository as fictional. Require commodity source attribution and documented assumptions; design role-aware access and separate scenario inputs from historical facts.

Evidence and outcomes

Source SQL, connection guidance, and a reporting interface evidence the engagement's technical architecture. Actual client adoption, accuracy against licensed market feeds, and realized financial benefit are not established by these assets.

Practical next steps

Document source terms, data lineage, Snowflake permissions, unit conversion, change tracking, quality tests and semantic-model reconciliation before promoting the reporting layer.

Explore the engagement

Explore the documented project on GitHub and request a private RUSA Analytics consultation to discuss an adapted solution. Source: https://github.com/RUSA-Analytics/snowflake-powerbi-data-warehouse