schema-evaluationlisted

# Schema Evaluation ## Purpose Evaluate and design data warehouse schemas for analytical workloads. Covers star schemas, snowflake schemas, data vault, and One Big Table (OBT) patterns. Assesses grain definition, normalization trade-offs, slowly changing dimension strategies, and data contracts between producers and consumers. ## Scope Constraints Reads schema definitions, DDL, ERDs, data dictionaries, and query patterns for analysis. Does not execute queries, modify databases, or manage pipeline orchestration. ## Inputs - Business domain and key entities (e.g., e-commerce: orders, products, customers) - Analytical queries the schema must support (e.g., "revenue by product category by month") - Data volume estimates (row counts, growth rate) - Source systems and their update patterns (CDC, full refresh, event stream) - Existing schema (if evaluating rather than designing from scratch) ## Input Sanitization No user-provided values are used in commands or file paths. All inputs are treated as read-only analysis targets. ## Procedure ### Progress Checklist - [ ] Step 1: Define the grain - [ ] Step 2: Identify facts and dimensions - [ ] Step 3: Choose a modeling approach - [ ] Step 4: Design SCD strategy - [ ] Step 5: Define data contracts - [ ] Step 6: Validate against query patterns - [ ] Step 7: Document the schema ### Step 1: Define the Grain Identify the grain of each fact table — what does one row represent? A single transaction? A daily snapshot? A session even