End-to-End SQL Data Warehouse with Dimensional and Analytics Layers

🚀 A hands-on learning project for building a modern SQL-based data warehouse from scratch

This project serves as a hands-on learning experience in building a modern data warehouse using SQL. It covers the entire data pipeline, from raw data ingestion (Bronze layer) to cleansed and transformed data (Silver layer), and culminates in the creation of business-ready reporting tables (Gold layer), modeled in a star schema.

This was created as a structured learning project, inspired by a tutorial , with the Analytics Layer being an original extension. It reflects real-world best practices in dimensional modeling, SQL transformations, and analytical data design — just like what you'd expect on a modern data engineering team.

Additionally, this project introduces an Analytics Layer, designed to empower business intelligence dashboards, advanced analytics, and machine learning models. The project is based on a tutorial, with the Analytics Layer being a custom extension aimed at providing deeper insights into customer behavior, product performance, and sales trends.

This project showcases core data engineering principles like dimensional modeling, ETL processes, and data transformation.

Architecture

The data warehouse follows a structured multi-layered approach:

1. Bronze Layer (Raw Data Ingestion)

• This is where raw data lands—as-is from different sources (databases, APIs, logs, files, etc.).

• No transformations, just ingestion. Think of it like a "dump zone" for incoming data.

• Schema: Loose or semi-structured.

• Example Tables: bronze.customer_raw, bronze.sales_raw.

2. Silver Layer (Cleaned & Transformed Data)

• Data is cleaned, deduplicated, and enriched.

• Standardized column names, formatted timestamps, and fixed data inconsistencies.

• Example: If gender is stored as M/F in one source and Male/Female in another, we make it consistent.

• Example Tables: silver.clean_customers, silver.clean_sales.

3. Gold Layer (Business-Friendly, Reporting-Ready Data)

• Data is shaped into a star schema (fact & dimension tables) for easy querying.

• Supports BI dashboards and self-service reporting.

• Example Tables:

  • Dimension Tables: gold.dim_customers, gold.dim_products

  • Fact Tables: gold.fact_sales

4. Analytics Layer (Advanced Analysis & Machine Learning)

• Pre-calculated KPIs & aggregations (customer lifetime value, product performance, trends).

• Designed for data scientists, analysts, and machine learning engineers.

• Example Views:

  • analytics.customer_lifetime_value: Who are our best customers?

  • analytics.product_performance_metrics: Which products are performing well?

  • analytics.sales_trends_and_forecasting: How are our sales trending over time?

Data Sources

The warehouse ingests data from multiple sources, including:

• CRM Systems → Customer & order details.

• ERP Systems → Product & inventory data.

• Sales Transactions → Order history & revenue.

• Web & Mobile Apps → User interactions & engagement metrics.

• Third-Party APIs → Market trends, external pricing, etc.

Key Features

✅ Scalable Architecture → Designed for large-scale analytics.✅ Optimized for BI & ML → Structured for easy reporting & data science workflows.✅ Automated Data Processing → ETL pipelines clean & enrich data automatically.✅ Historical & Trend Analysis → Enables predictive analytics & business insights.

Usage & Queries

The data warehouse supports various use cases:

1. Business Intelligence (BI Dashboards)

• Connect tools like Power BI, Tableau, or Looker to the Gold & Analytics layers.

• Example Query: Monthly Sales Report

SELECT FORMAT(order_date, 'yyyy-MM') AS month, SUM(sales_amount) AS total_sales FROM gold.fact_sales GROUP BY FORMAT(order_date, 'yyyy-MM') ORDER BY month; `

2. Machine Learning & Advanced Analytics

• Train ML models on customer purchase behavior, sales forecasting, etc.

• Example: Predicting High-Value Customers (using analytics.customer_lifetime_value).

• Example Query:

SELECT customer_id, lifetime_spend, total_orders, customer_segment FROM analytics.customer_lifetime_value WHERE customer_segment = 'High-Value Active'; `

Setup & Deployment

1. Database Setup

   • Run the DDL scripts for bronze, silver, gold, and analytics layers.

   • Ensure data ingestion processes (ETL pipelines) are set up.

2. ETL Workflow

   • Use Airflow, DBT, or custom scripts to automate transformations.

   • Schedule periodic updates to ensure fresh data.

3. BI & Reporting Tools

   • Connect BI tools to gold and analytics schemas.

   • Build dashboards using pre-aggregated metrics.

Security Features and Access Control

Security is a critical aspect of any data warehouse, especially when dealing with sensitive business and customer data. In this project, we've implemented robust security features to ensure that only authorized users can access specific datasets and perform certain actions.

Key Elements:

• Roles and Permissions:

  • We have created distinct roles for different user groups such as Data Analysts, Data Engineers, and Business Users. These roles have specific permissions that govern what data they can access and what operations they can perform.
  • Data Engineers are granted access to the raw data and are responsible for managing the transformation pipeline, whereas Business Users only have access to the Gold and Analytics layers for reporting and analysis.

• Access Control:

  • Through role-based access control (RBAC), users can only query or modify data within their scope of responsibility, minimizing the risk of unauthorized data access or accidental changes.
  • Access is granted via secure views, where business-friendly data is made available without exposing raw data directly to users.

Future Enhancements

🔹 Implement real-time streaming for near real-time analytics.🔹 Introduce a Data Lakehouse approach for handling unstructured data.🔹 Deploy ML models directly within the warehouse (e.g., Snowflake ML, BigQuery ML).🔹 Optimize query performance with materialized views & indexing.

Conclusion

This data warehouse is designed to transform raw data into valuable insights efficiently. Whether for business intelligence, machine learning, or trend analysis, it provides a structured and scalable foundation for data-driven decision-making. 🚀