nw-data-architecture-patterns

# Data Architecture Patterns ## Architecture Selection Decision Tree Structured only -> **Data Warehouse** | Mixed + SQL analytics -> **Data Lakehouse** | Mixed + ML-primary -> **Data Lake** | Large org + autonomous domains -> **Data Mesh** ## Data Warehouse Schema: structured, schema-on-write | Data: tables, rows, columns | Governance: centralized | Query: SQL analytics, BI | Architecture: centralized single source of truth ### Schema Patterns **Star Schema**: Central fact table (measures) surrounded by denormalized dimension tables. Best for BI dashboards, standard reporting. **Snowflake Schema**: Normalized dimensions (dimensions reference other dimensions). Reduces storage, increases JOIN complexity. Best when storage cost matters more than query speed. ### Kimball vs Inmon **Kimball (Bottom-Up)**: Build data marts first, integrate later | Star schema, business-process driven | Faster initial delivery | Best for quick wins, department-level analytics **Inmon (Top-Down)**: Build enterprise DW first, derive data marts | Normalized 3NF enterprise model | Higher upfront effort | Best for large enterprises needing single source of truth Technology: Snowflake | Amazon Redshift | Google BigQuery | Azure Synapse Analytics ## Data Lake Schema-on-read, flexible | All formats (structured, semi-structured, unstructured) | Raw data in native format | Query via Athena, Spark SQL, PySpark, Pandas | Risk: "data swamp" without governance ### Organization Zones: **raw** (land...