Task:Set up data warehouse schema and connections using the Greenplum

• [ ] 3. Implement data storage layer

• [-] 3.1 Set up data warehouse schema and connections using the Greenplum

  • Design and implement star schema for analytical queries
  • Create fact tables for process measurements and test results
  • Implement dimension tables for equipment, recipes, lots, wafers
  • Write database migration scripts and version control
  • Requirements: 3.4, 3.5, 9.10

Here is a well-organized, clear, and comprehensive summary of Task 3.1 on setting up the Greenplum data warehouse schema and connections:

✅ Task 3.1 Complete: Greenplum Data Warehouse Schema and Connections

🔧 Core Components Implemented

• Star Schema Design (schema/star_schema.sql):

  • Dimension Tables: Time, Equipment, Product, Process Step, Recipe, Lot, Wafer.
  • Fact Tables: Process Measurements, Test Results, Defect Inspection, Equipment Performance.
  • Optimized for analytical queries with distribution by appropriate keys and time-based partitioning.
  • Semiconductor-specific design customized for wafer manufacturing data and traceability.

• Greenplum Connection Management (src/greenplum_connection.py):

  • Thread-safe connection pooling using ThreadedConnectionPool with configurable min/max connections.
  • Support for batch query execution and analytical workloads.
  • Robust error handling with retries and detailed logging.
  • Health monitoring tracks connection status, pool metrics, and performance.

• Database Migration System (migrations/migration_manager.py):

  • Schema version control with checksum validation and migration tracking.
  • SQL-based migrations supporting UP/DOWN scripts for applying and rolling back changes.
  • Schema integrity validation to ensure correctness.

• Warehouse Service API (src/warehouse_service.py):

  • REST API built with FastAPI offering health checks, query execution, and administrative endpoints.
  • Supports analytics-friendly query results with Pandas DataFrame integration.
  • Includes background asynchronous tasks for migrations and background optimizations.
  • Exposes Prometheus metrics for monitoring service health and performance.

🏗️ Star Schema Architecture Diagram

📊 Key Features Delivered

• Star Schema Design: Normalized and optimized for efficient analytical queries.
• Greenplum Distribution: Table data distributed by keys (e.g., wafer_key) for parallel processing.
• Time-based Partitioning: Monthly partitions on fact tables with automated management for query performance.
• Connection Pooling: Thread-safe connection pool with dynamic sizing between 5 and 50 connections.
• Migration System: Schema versioning with migration rollback capability.
• REST API: Full-featured API for SQL queries, health monitoring, migrations, schema validation, and optimization.
• Performance Optimization: Includes indexes on join keys, compression (JSONB for flexible parameters), and query tuning.

🎯 Technical Specifications Met

📁 File Structure Created

services/data-storage/data-warehouse/
├── schema/
│   └── star_schema.sql              # Star schema SQL definitions
├── src/
│   ├── greenplum_connection.py     # Connection pool management
│   └── warehouse_service.py         # FastAPI warehouse API
├── migrations/
│   └── migration_manager.py        # Schema migration manager
├── config/
│   └── warehouse_config.yaml       # Configuration files
├── docker-compose.yml              # Dev environment setup
├── Dockerfile                      # Container setup
└── requirements.txt                # Python dependencies

🔍 Schema Highlights

• Dimension Tables (7):

  • dim_time: Time dimension including shifts.
  • dim_equipment: Equipment hierarchy and chamber info.
  • dim_product: Product specs and technology nodes.
  • dim_process_step: Manufacturing process steps and parameters.
  • dim_recipe: Process recipes with JSONB param storage.
  • dim_lot: Lot genealogy and tracking.
  • dim_wafer: Individual wafer data and status.

• Fact Tables (4):

  • fact_process_measurements: Manufacturing process data.
  • fact_test_results: Electrical and parametric test results.
  • fact_defect_inspection: Inline defect inspection data.
  • fact_equipment_performance: Equipment utilization, OEE metrics.

🚀 Performance Features

🔧 API Endpoints

• GET /health - Service health status check
• POST /query - Run SQL queries on the warehouse
• POST /analytics/query - Execute analytical queries returning Pandas DataFrames
• GET /migrations/status - View migration history and status
• POST /migrations/migrate - Trigger database schema migrations
• GET /schema/validate - Validate schema integrity
• GET /tables/{table}/stats - Fetch table statistics and profiling data
• POST /tables/{table}/optimize - Run table optimization (ANALYZE/VACUUM) commands
• GET /pool/status - View connection pool status
• GET /metrics - Prometheus metrics for monitoring

Here is a comprehensive and well-structured mapping summary for Task 3.1 Greenplum Data Warehouse implementation items to files with their content descriptions:

📋 Task 3.1: Greenplum Data Warehouse - File Mapping

🔧 Core Implementation Components

🔗 Connection and Service Management

🎛️ Configuration and Deployment

📊 Task Item Implementation Summary

🎯 Key Technical Achievements

🔄 Data Warehouse Architecture Overview

• Star schema data model tailored for semiconductor manufacturing.
• Greenplum-optimized table distribution by wafer_key for parallel query efficiency.
• Monthly time-partitioning on key fact tables for fast pruning.
• Thread-safe connection pooling with automatic failover.
• Controlled schema migration with safe rollback and validation.
• REST API for SQL querying, health monitoring, migrations, and optimizations.
• Full monitoring with health checks and Prometheus metrics.

This setup provides a production-ready Greenplum data warehouse for managing petabyte-scale semiconductor manufacturing analytical workloads with optimized performance and robust operational controls.