A SQL Server Data Warehouse built using the Medallion Architecture (Bronze → Silver → Gold), integrating multi-source data into analytics-ready dimensional models.
- Project Overview
- Architecture
- Data Sources
- Data Layers
- Data Flow & Lineage
- ETL Workflow
- Gold Layer Outputs
- Tech Stack
- Author
BusinessDW is a comprehensive Business Data Warehouse built on SQL Server that integrates data from two enterprise source systems — CRM and ERP — and transforms raw operational data into structured, analytics-ready information.
The project follows the Medallion Architecture — a layered data design pattern that ensures traceability, data quality, and business-readiness at each stage of the pipeline.
Key Goals:
- Integrate multi-source enterprise data (CRM + ERP)
- Build a reliable, auditable ingestion pipeline
- Apply data cleansing, standardization, and business rules
- Deliver dimensional models ready for BI, reporting, and analytics
sqlserver-datawarehouse/
│
├── DataSource/ # Raw source datasets from CRM and ERP systems
│ ├── CRM/
│ │ ├── cust_info.csv # Customer master data
│ │ ├── prd_info.csv # Product information
│ │ └── sales_details.csv # Sales transaction records
│ │
│ └── ERP/
│ ├── CUST_AZ12.csv # ERP customer reference data
│ ├── LOC_A101.csv # Customer location mapping
│ └── PX_CAT_G1V2.csv # Product category reference data
│
├── docker/
│ └── requirements-local.txt # Local development dependencies
│
├── Docs/ # Architecture diagrams and project documentation
│ ├── BusinessDW_Digram.png # Business-oriented warehouse architecture
│ ├── data_architecture.png # End-to-end data architecture
│ ├── Data_Flow.png # Data ingestion and transformation flow
│ ├── data_layers.pdf # Medallion architecture layer explanation
│ ├── gold_data_model.gif # Gold layer dimensional model
│ └── relation_digram.gif # Entity relationship diagram
│
├── LICENSE
├── README.md
│
├── Script/
│ │
│ ├── Analysis/ # Data analysis and reporting queries
│ │ ├── dashboard_query/
│ │ │ └── dashboard_query.sql # Dashboard and KPI queries
│ │ │
│ │ ├── data_analysis/
│ │ │ └── qulity_check.sql # Data quality validation queries
│ │ │
│ │ ├── data_analysis.ipynb # Exploratory data analysis notebook
│ │ └── report/ # Reporting scripts and outputs
│ │
│ ├── Bronze/ # Raw ingestion layer
│ │ ├── ddl_bronze.sql # Bronze schema/table definitions
│ │ └── proc_bronze.sql # Bronze ingestion stored procedures
│ │
│ ├── Silver/ # Cleansed and transformed data layer
│ │ │
│ │ ├── transform/
│ │ │ ├── ddl_silver.sql # Silver layer schema definitions
│ │ │ ├── dirgram.puml # Silver layer transformation diagram
│ │ │ └── proc_load_silver.sql # Silver ETL procedures
│ │ │
│ │ └── transformation/
│ │ ├── crm_transformation/ # CRM data transformation logic
│ │ │
│ │ │ ├── customers/
│ │ │ │ ├── cust_info.md
│ │ │ │ └── cust_info.sql
│ │ │ │
│ │ │ ├── products/
│ │ │ │ ├── prd_info.md
│ │ │ │ └── prd_info.sql
│ │ │ │
│ │ │ └── sales/
│ │ │ ├── sales_details.md
│ │ │ └── sales_details.sql
│ │ │
│ │ └── erp_transformation/ # ERP data transformation logic
│ │ ├── customers/
│ │ │ ├── erp_cust_az12.md
│ │ │ └── erp_cust_az12.sql
│ │ │
│ │ ├── locetion/
│ │ │ ├── erp_loc_a101.md
│ │ │ └── erp_loc_a101.sql
│ │ │
│ │ └── product/
│ │ ├── px_cat_g1v2.md
│ │ └── px_cat_g1v2.sql
│ │
│ ├── Gold/ # Business-ready analytical layer
│ │ ├── data_modle.puml # Gold dimensional model design
│ │ ├── ddl_gold.sql # Gold schema/view definitions
│ │ │
│ │ └── model/
│ │ ├── dim_customers.sql # Customer dimension
│ │ ├── dim_products.sql # Product dimension
│ │ └── fact_sales.sql # Sales fact table
│ │
│ └── init_database.sql # Database initialization script
│
├── SECURITY.md
├── setup_command.sh # Local project setup commands
│
└── superset/ # Apache Superset dashboards and BI integration
The data warehouse is structured as a SQL Server database with three schema layers, each serving a distinct purpose in the data pipeline.
Data is ingested from two enterprise systems via CSV file extracts (batch ingestion):
| File | Description |
|---|---|
cust_info.csv | Customer master information |
prd_info.csv | Product details |
sales_details.csv | Transactional sales / order records |
| File | Description |
|---|---|
cust_az12.csv | Customer demographics — birth date, gender |
loc_a101.csv | Customer geography — country mapping via customer ID |
px_cst_g1v2.csv | Product classification — category, subcategory, maintenance indicator, product line |
| Property | Detail |
|---|---|
| Definition | Raw, unprocessed data — as-is from source systems |
| Objective | Traceability & Debugging |
| Object Type | Tables |
| Load Method | Full Load (Truncate & Insert) |
| Transformations | None (as-is) |
| Data Modeling | None (as-is) |
| Target Audience | Data Engineers |
The Bronze layer preserves the exact source data to enable full auditability and root-cause analysis for any downstream data issues.
| Property | Detail |
|---|---|
| Definition | Cleaned & standardized data |
| Objective | Intermediate layer — prepare data for analysis |
| Object Type | Tables |
| Load Method | Full Load (Truncate & Insert) |
| Target Audience | Data Analysts, Data Engineers |
Transformations applied:
- Data Cleansing
- Data Standardization
- Data Normalization
- Derived Columns
- Data Enrichment
Key business rules:
- Deduplicate customers
- Standardize product IDs and names
- Conform customer IDs across ERP and CRM
- Map country codes
- Enrich products with classification attributes
- Validate date and gender values
- Cleanse sales transactions
| Property | Detail |
|---|---|
| Definition | Business-Ready data |
| Objective | Provide data for reporting & analytics consumption |
| Object Type | Views |
| Load Method | None (derived from Silver) |
| Target Audience | Data Analysts, Business Users |
Transformations applied:
- Data Integration (joining across domains)
- Data Aggregation
- Business Logic & Rules
Data Modeling patterns:
- Star Schema
- Aggregated Objects
- Flat Tables
Each Bronze table maps 1:1 to a Silver table. Silver tables are then integrated and aggregated into Gold views using business logic and dimensional modeling.
Each layer follows a consistent 4-phase development workflow:
Analyse Code Validate Document
───────────────── ───────────────── ────────────────── ──────────────────
Interview source ──▶ Data Ingestion ──▶ Completeness & ──▶ Documenting
system experts (Stored Proc) Schema Checks Versioning in GIT
Analyse Code Validate Document
───────────────── ───────────────── ────────────────── ──────────────────
Explore & ──▶ Data Cleansing ──▶ Data Correctness ──▶ Documenting
Understand Data (Stored Proc) Checks Versioning in GIT
+ Data Flow
+ Data Integration
Analyse Code Validate Document
───────────────── ───────────────── ────────────────── ──────────────────
Explore & ──▶ Data ──▶ Data Integration ──▶ Documenting
Understand Integration Checks Versioning in GIT
Business Objects (Stored Proc) + Data Model
+ Data Catalog
+ Data Flow
The following analytics-ready objects are produced in the Gold layer:
| Object | Type | Description |
|---|---|---|
dim_customers | View | Unified customer dimension — integrates CRM + ERP demographics and geography |
dim_products | View | Enriched product dimension — integrates product details with classification data |
dim_location | View | Geographic dimension for location-based analytics |
fact_sales | View | Central sales fact table — transactional order records |
These objects form a Star Schema optimized for BI tools, ad-hoc SQL queries, and machine learning pipelines.
| Component | Technology |
|---|---|
| Database | SQL Server |
| ETL / Transformation | T-SQL Stored Procedures |
| Load Strategy | Batch Processing — Full Load (Truncate & Insert) |
| Source Interface | CSV files (folder-based ingestion) |
| Version Control | Git |
| Consumers | BI & Reporting, Ad-Hoc SQL, Machine Learning |
Ritik Aspiring Data Engineer | Focused on building production-grade data systems
This project is a complete end-to-end Business Data Warehouse implementation — from raw data ingestion to business-ready analytical datasets. It demonstrates real-world data engineering practices including multi-source integration, layered ETL design, data quality management, and dimensional modeling using industry-standard Medallion Architecture principles.
Built with SQL Server · Medallion Architecture · CRM & ERP Integration