Databricks x Sigma QuickStart

Sigma brings true self-service analytics to the Databricks Lakehouse by allowing business users to explore and analyze Delta tables directly, without extracts, duplicated semantic layers, or BI-specific data marts.

Where Databricks excels at engineering, AI, and large-scale transformations, Sigma completes the stack by making governed lakehouse data usable by business teams in real time.

This QuickStart accelerates the path from Databricks data platform to faster business decision-making.

How this solution works

Stage 1 – Analytics Use Case Alignment (1 week)

You align business questions with Delta tables already available in Databricks. The focus is on operational, financial, and product use cases where teams need fast, flexible analysis without engineering dependency.

You explicitly identify where dashboards are not enough and where spreadsheet-style exploration adds value.

Outcome:
A prioritized list of Sigma-ready analytics use cases mapped to Databricks schemas and personas.

Stage 2 – Lakehouse Analytics Readiness (1 week)

You assess Databricks SQL Warehouses, Unity Catalog setup, table structures, and access patterns. Because Sigma queries Databricks directly, you ensure performance, permissions, and cost controls are ready for interactive analytics.

Outcome:
A Databricks environment optimized for business-facing analytics on top of the lakehouse.

Stage 3 – Sigma + Databricks Technical QuickStart (1–2 weeks)

You connect Sigma to Databricks SQL, configure authentication (SSO), map Unity Catalog permissions, and validate query performance. Core Delta tables are exposed without data replication or transformation.

Outcome:
A live Sigma environment running natively on Databricks SQL Warehouses.

Stage 4 – Metrics, Semantics & Self-Service (1–2 weeks)

You create Sigma datasets and metrics directly on Delta tables. Business logic is defined once and reused, while all calculations execute in Databricks. Users work in a familiar spreadsheet interface without needing SQL.

Outcome:
A governed, reusable analytics layer on top of the Databricks Lakehouse.

Stage 5 – Adoption, Enablement & Cost Control (1 week)

You train business teams, define usage KPIs, and establish best practices for warehouse sizing and concurrency. This ensures analytics adoption scales without driving unexpected Databricks costs.

Outcome:
High Sigma adoption, controlled Databricks spend, and measurable business value.

Key outcomes

1. Business-friendly analytics directly on Databricks

2. No data extracts, no BI-specific data marts

3. Unified governance via Unity Catalog

4. Reduced dependency on data engineers for analytics

5. Faster ROI from Databricks investments