Modern analytics using the Lakehouse architecture.

📌 Introduction

Dimensional Data Warehousing has long been the foundation of business intelligence and reporting systems. But today, data is bigger, faster, and messier than ever before.

That’s where Databricks SQL comes in combining the flexibility of data lakes with the performance of data warehouses. In this post, we’ll explore how to design and implement a Dimensional Data Warehouse using Databricks SQL, even if you’re just getting started.

What is a Dimensional Data Warehouse?

A Dimensional Data Warehouse stores data in a structure optimized for querying and reporting. It uses:

• Fact Tables : Central tables with measurable business metrics (sales, revenue, etc.)
• Dimension Tables : Descriptive attributes (time, product, location, etc.)

This is known as the Star Schema or Snowflake Schema depending on complexity.

Why Use Databricks SQL?

Databricks SQL brings a modern Lakehouse architecture to dimensional modeling. Benefits include:

• 🔄 Seamless integration with Delta Lake for ACID transactions
• 🚀 Lightning-fast queries with Photon Engine
• 🧩 SQL-native experience for analysts and engineers
• 🛠️ Built-in dashboards for BI-style visualizations

Step-by-Step: Building a Dimensional Warehouse in Databricks

✅ Step 1: Understand Your Business Use Case

Let’s say we want to analyze e-commerce sales. Key questions:

• What are our monthly sales?
• Which regions or products perform best?
• What’s our customer retention rate?

These drive our fact and dimension table design.

✅ Step 2: Create Your Schema (Star/Snowflake)

Example Tables:

• Fact_Sales: order_id, product_id, customer_id, store_id, date_id, quantity, total_amount
• Dim_Product: product_id, name, category, brand
• Dim_Customer: customer_id, name, segment, signup_date
• Dim_Date: date_id, date, month, quarter, year
• Dim_Store: store_id, location, region, manager

✅ Step 3: Load Data into Delta Tables

You can ingest from CSV, Parquet, or external databases (e.g., MySQL, PostgreSQL):

sql
CREATE OR REPLACE TABLE dim_customer
USING DELTA AS
SELECT * FROM parquet.`/mnt/data/customers.parquet`;

For incremental updates, use Auto Loader or Structured Streaming.

✅ Step 4: Apply Business Logic and Transformations

Use SQL to cleanse and model data:

sql
-- Calculate total sales per product
SELECT
  p.category,
  SUM(f.total_amount) AS total_sales
FROM fact_sales f
JOIN dim_product p ON f.product_id = p.product_id
GROUP BY p.category;

Build views for monthly reports, trend analysis, or customer behavior.

✅ Step 5: Create Visual Dashboards

With Databricks SQL UI, you can:

• Build custom dashboards
• Set alerts for KPIs
• Share insights securely with your team

Example: Show a heatmap of sales by region and month.

📊 Real-World Use Case

A retail company used Databricks SQL to move from a traditional SQL Server warehouse to a Lakehouse. They saw:

• 5x faster reporting performance
• 50% reduction in cloud costs
• Easier scaling as data grew from GBs to TBs

✅ Best Practices

• Use surrogate keys in dimension tables
• Keep fact tables narrow and indexed
• Use partitioning and Z-Ordering on Delta tables for performance
• Implement CDC (Change Data Capture) where possible

Conclusion

Combining dimensional modeling with Databricks SQL gives you the best of both worlds: familiar business logic with modern cloud scalability.

Whether you’re migrating from a legacy DWH or building new, Databricks lets you simplify your stack and supercharge insights.

DATABRICKS COURSE: 

AccentFuture’s Databricks Training  designed for analysts, engineers, and aspiring data professionals.

Learn hands-on with real-world projects covering Delta Lake, Spark, and Databricks SQL.

Master ETL, data engineering, and Lakehouse architecture to accelerate your career in data.