Google BigQuery x Sigma QuickStart

Sigma brings governed, self-service analytics directly on top of Google BigQuery, enabling business users to explore massive datasets without extracts, replicas, or BI-specific data layers.

Where BigQuery excels in serverless analytics, elasticity, and scale, Sigma completes the stack by giving business teams an intuitive, spreadsheet-like interface that runs natively on BigQuery compute.

This QuickStart accelerates the path from BigQuery datasets to faster business decisions, without introducing complexity or operational overhead.

How the solution works

Stage 1 – Analytics Use Case Alignment (1 week)

You align business questions with BigQuery datasets that are already available or nearly production-ready. The focus is on use cases that benefit from BigQuery’s scale and concurrency, such as finance, growth, marketing, and operational analytics.

You explicitly identify where dashboard-only BI falls short and where ad-hoc, spreadsheet-style exploration adds value.

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

Stage 2 – BigQuery Analytics Readiness (1 week)

You assess dataset structures, partitioning and clustering strategies, IAM roles, and cost controls. Because Sigma executes queries directly in BigQuery, special attention is paid to query patterns, cost visibility, and concurrency management.

Outcome:
A BigQuery environment optimized for interactive, business-facing analytics.

Stage 3 – Sigma × BigQuery Technical QuickStart (1–2 weeks)

You connect Sigma to BigQuery, configure authentication (SSO), map IAM permissions, and validate performance on representative workloads. Core datasets are exposed without copying or transforming data.

Outcome:
A live Sigma environment running natively on Google BigQuery.

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

You define reusable metrics, calculations, and business logic directly in Sigma on top of BigQuery tables and views. Business users gain Excel-like flexibility while all processing remains serverless in BigQuery.

Outcome:
A governed self-service analytics layer powered by BigQuery.

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

You train business teams, establish usage KPIs, and define best practices for managing BigQuery costs (slot usage, query limits, caching strategies). This ensures analytics adoption scales without cost surprises.

Outcome:
High Sigma adoption, predictable BigQuery spend, and measurable analytics ROI.

Getting Started

1. Business-friendly analytics directly on BigQuery

2. Fully serverless, no infrastructure management

3. No data extracts or BI-specific replicas

4. Strong governance via BigQuery IAM

5. Faster insight at massive scale