connect to the data platforms you already operate on

Architected for live warehouse data

Sigma is a spreadsheet UI on top of your cloud data warehouse. Filters, group-bys, pivots, and formulas compile into SQL in your warehouse's dialect, run where the data lives, and return results to the browser.

Live query only (no extracts)

Run directly on your warehouse, not a separate data store. Control caching and refresh so teams get speed without losing freshness.

Spreadsheet actions compile into SQL

Work in a spreadsheet UI while Sigma generates optimized SQL behind the scenes. Inspect the SQL, execution path, and timing details when you need to troubleshoot.

Governance stays at the warehouse boundary

Access is enforced at query time with warehouse roles like OAuth or service accounts. Add platform permissions plus audit logs for who changed what.

Architecture at a glance

Execute everything inside the warehouse boundary.

When anyone opens a workbook, Sigma plans what data needs to be fetched and compiles operations into machine-optimized SQL.

Sigma then decides the best execution path, whether it's cached results, in-browser calculations, or pushdown to the warehouse.

Warehouse-native execution

All AI processing runs on your cloud data warehouse compute.

Inherited security

AI respects existing row-level security and permissions.

Deterministic outputs

Reproducible results with consistent behavior.

End-to-end lineage

Full visibility into data transformations and AI operations.

Under the hood

See the parts architects ask about: compilation, execution paths, governance boundaries, and what you can measure.

Transformation

Sigma translates spreadsheet operations into SQL on the fly. Switch statements become CASE logic, moving averages become window functions, and pivots compile to your warehouse's dialect.

Query History shows the generated SQL for every element, with timing breakdowns and request IDs for warehouse tuning.

Inspect Query History

Execution paths

Sigma exposes query behavior including queue time, Sigma runtime, warehouse runtime, and result fetch time, plus admin usage dashboards and audit logs.

Built-in templates include cost/performance reporting down to useful cut lines like workbook, query, and user.

Explore warehouse usage templates

Governance & Roles

Sigma can run as the user (OAuth) or as a service account, and optionally map users/teams to warehouse roles so your warehouse policies are enforced at query time.

You can also define access rules in Sigma to control access to content, data, and features.

Data access overview

Execution Paths

Not every click should wake up your warehouse. Sigma’s hybrid query engine evaluates the fastest, lowest-cost execution path by starting in the browser, then escalating through query ID caching, and only then to the warehouse.

Reuse results already in the user’s browser session. If Sigma can satisfy the request from what’s already been returned, there’s no network roundtrip, and no new warehouse work.

When the data is in-cache but the shape changed, Sigma recalculates “what changed” directly in the browser—think: new calcs, filters, regrouping—before issuing anything to the warehouse.

Sigma fingerprints the query structure and keeps a mapping to the warehouse query ID. If it’s been run before (and your platform supports result caching), Sigma can fetch the cached result via that ID without storing your query results in Sigma.

If Sigma has to go back to the warehouse, the warehouse can still return results from its own cache when conditions allow (many platforms keep result caching for up to ~24 hours depending on changes and determinism).

Only then do you light up compute. But Sigma pushes optimized SQL and merges work when possible to reduce the number of separate hits needed to render a full page of tables, charts, and pivots.

Your data stays in your warehouse storage. And for repeat-heavy logic, materialize expensive datasets into reusable warehouse tables and refresh on your schedule, so “fresh” doesn’t have to mean “expensive.”

Read the decision tree + caching tiers

Request a Demo

Try Sigma for free

From Data to Revenue and Operational Outcomes

Sigma supports the core workflows where sales performance, capacity, and execution determine growth.

Write back to the warehouse to annotate, adjust, or contribute data.

Edit data in Sigma. Write it back to the warehouse.

Add rows or update values in a Sigma table UI

Persist changes to a warehouse table (INSERT / UPDATE / DELETE)

Downstream models and dashboards reflect updates immediately

Learn about Input Tables

Precompute expensive logic when it makes sense.

Precompute expensive transformations and query the output table.

Persist results as a warehouse table instead of recomputing each query

Speed up downstream dashboards and app workflows

Keep logic consistent anywhere it’s reused

About materialization

Modular governance using Role-Based Access Control (RBAC)

Reuse metrics you already define.

If your team standardizes metrics in dbt, Sigma can query the dbt Semantic Layer so builders aren't redefining business logic in five different places.

Query a dbt Semantic Layer integration

Accurate insights across your data stack

Enterprise-Grade Analytics for Sales & Ops Scale

Sigma is built for business teams that need flexibility without sacrificing governance or performance.

Visit Trust Center

Zero-copy query model

Sigma doesn't require you to duplicate warehouse tables into a separate store to get interactivity. Results can be reused via cache paths instead of persisting a separate copy.

Private connectivity (AWS/Azure/GCP)

Support for PrivateLink / Private Service Connect patterns when your security team wants to keep traffic off the public internet.

Auth or service account

Run per-user (OAuth) or via a service account. Choose what fits your governance model and auditing requirements.

Role-aware access control

Dynamically map users/teams to warehouse roles so row/column policies are enforced at query time.

Audit logs for admin events

Track key admin activities (logins, permission changes, connection changes, and more) for operational visibility.

Compliance artifacts in the Trust Center

SOC 2 Type II, ISO/IEC 27001, GDPR/CCPA posture, and other reports live in one place for review.

Fits into the rest of your stack

Sigma connects to your warehouse, and it also plays well with the systems around it whether its catalog, transformation, monitoring, or reverse ETL.

Reuse standardized metrics and keep business logic centralized.

Let users discover governed tables and definitions where they already look.

Keep an eye on pipeline and data quality issues that impact downstream analysis.

Operationalize curated outputs from the warehouse into downstream tools.

See our partner integrations

Real customers, real workloads

From insight to action—powered by the warehouse.

700+

Active users

Days to Hours

In time savings

"I think the biggest value driver for Sigma is that you're not using specialized Python developers to analyze billion row records anymore. You're just adding an Excel user."

Han Zhang

Senior Vice President, Blackstone

READ THE STORY

10+

New capabilities introduced

50+

Data sources flowing into Snowflake hourly

"Post-Sigma, real-time dashboards and automated data pipelines improved decision-making speed by 40%. This shift led to a 15% increase in revenue from optimized campaigns."

READ THE STORY

75%

Employee adoption

80%

Reduction in reporting time

Yamaha Motor Finance replaced spreadsheets and manual reporting with Sigma’s governed, cloud-native platform. See how they transformed FP&A, enabled self-service, and scaled financial modeling with Snowflake and Sigma.

READ THE STORY

Discover how RightRev, a pioneer in automated revenue recognition, overcame data challenges and improved user experience by integrating Sigma’s advanced analytics, enabling seamless reporting and empowering finance teams to work smarter.

Jagan Reddy

CEO, RightRev

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80%

Decrease in manual reporting

Days to minutes

Drop in time to insight

Discover how Scribe’s workflow AI platform scaled its data culture with Sigma — cutting reporting time by 80% and empowering every team with self-service insights.

READ THE STORY

Architecture FAQ

The questions that usually come up once someone starts mapping Sigma into their warehouse and governance model.

Does every click run a warehouse query?

No. Some interactions are satisfied from the browser cache, some are computed in the browser (Alpha Query), some reuse warehouse cached results via query ID, and some run as fresh warehouse queries. You can see the execution path per query in Query History.

What is warehouse-native architecture?

It means Sigma runs on top of your data warehouse, so queries execute where your data already lives.

How do you keep warehouse compute from getting out of hand?

Two levers: (1) the query engine (cache tiers + in-browser compute) is designed to avoid unnecessary queries during exploration, and (2) teams materialize expensive/reused logic back into the warehouse so downstream workbooks hit precomputed tables.

Can we see the actual SQL Sigma is generating?

Yes. Query History shows the generated SQL, timing breakdown, and request ID so you can troubleshoot with support or tune the warehouse side.

What does “inside the warehouse boundary” mean?

Your warehouse stays the system of record, and access is enforced at query time under your existing roles and policies.

What does “zero-copy” mean?

Sigma doesn’t duplicate your warehouse tables into a separate data store just to make dashboards fast.

How do roles / RLS / CLS work?

Sigma enforces permissions in two places: your warehouse (via OAuth run-as-user and roles) and directly in Sigma (via row-level security and column-level security). You can map users/teams to warehouse roles using attributes, and optionally run a published workbook with a service account when needed.

What does "private connectivity" actually mean here?

If you use private networking, Sigma supports common patterns like AWS PrivateLink, Azure Private Link, and GCP Private Service Connect. Setup is a joint networking exercise (yours + Sigma's), and it's documented step-by-step.

What about data freshness?

You can control cache duration / TTL, define acceptable staleness for a workbook, and bypass caches when you explicitly need the latest results.

Where does AI processing run in Sigma?

Sigma runs AI on warehouse compute and returns the results to the app, without pulling raw data into a separate engine.

Architected for live warehouse data

Architecture at a glance

Under the hood

Workbooks generate warehouse-optimized SQL

You can attribute spend and tune from real usage.

Drive access from the warehouse, Sigma, or both

You pay for queries. Not curiosity.

From Data to Revenue and Operational Outcomes

Write back to the warehouse to annotate, adjust, or contribute data.