Data Warehouse BI Tool

This tool enables modeling and querying within a data warehouse BI workflow. It targets data engineers, analysts, and business users who require reliable, governance-friendly metrics drawn from centralized data. When building dashboards, validating data quality, or performing dimensional analysis across star/snowflake schemas, the tool provides metadata-driven schema discovery, dimensional modeling checks, and generated query templates to reduce manual SQL, speed iteration, and improve consistency across reports.

Core capabilities include metadata ingestion from warehouse catalogs, automatic schema discovery, dimensional modeling validation, and generation of optimized aggregate queries. Required features: connect to a data warehouse, define facts (measures) and dimensions, establish relationships, and produce ready-to-run SQL. Optional/advanced features: anomaly detection, cohort analysis, incremental refresh planning, cross-warehouse joins, and AI-assisted recommendations. Inputs include connection string, target schema, list of facts, dimensions, measures, filters, time window, and optional transformation rules. Outputs include generated SQL/MDX-like queries, analytics-ready datasets, and export options (CSV, Parquet) for dashboards and reports.

Applied algorithms follow dimensional modeling rules (facts with additive measures, dimensions with attributes), support star/snowflake schema generation, historical snapshot handling (Type 2), and efficient aggregation pipelines; ANSI SQL dialect mapping per warehouse (PostgreSQL, Snowflake, BigQuery) optimizes execution. Assumptions include access to warehouse metadata; exclusions cover front-end visualization and full ETL orchestration. Localization considers currency and date formats per locale and regulatory data governance needs.