Sigma vs Snowflake and Databricks
Keep your warehouse. Skip the backlog.
Snowflake and Databricks give you storage and compute. Sigma does the engineering on top: ingestion, modeling, cleaning, upkeep and the workflows your teams use.
Backed by Y Combinator
Before Snowflake answers in plain English
44 steps
written in SQL by someone on your team: 15 to transform the data, 14 to model it, 15 for the semantic layer.
Sigma does them for you
The work in between
Every step between raw tables and a plain-English answer.
Here’s what it takes on Snowflake today. Databricks follows a similar path with its own configuration.
15
Transform
Make different systems talk to each other.
Data profiling
Schema mapping
Type standardization
Data cleaning
Field normalization
Value harmonization
Deduplication
Entity resolution
Universal-ID assignment
Cross-linking
Business-rule standardization
Metric calculation
Aggregation and feature creation
Data-quality validation
Publish the business-ready layer
14
Model
Define what each business object is.
Choose the business domain
List the required business questions
Select normalized sources
Define business objects
Define each object’s grain
Identify unique keys
Choose authoritative records and fields
Define relationships
Apply business rules
Standardize business categories
Create tables and views
Validate grains and relationships
Reconcile business totals
Publish the governed model
15
Semantic layer
Teach the warehouse your vocabulary.
Select business-ready sources
Register logical tables
Document grains and keys
Declare semantic relationships
Define dimensions
Define facts
Define metrics
Define filters and time concepts
Add descriptions and synonyms
Create the semantic view
Configure security
Test natural-language questions
Add verified queries
Attach it to a Cortex Agent
Publish and monitor
With Sigma
All 44, done for you.
Sigma works them out from your field names, the logic already in your systems, your docs and email, and a short questionnaire. When a match isn’t certain, it asks your team instead of guessing. No SQL to read or write.
The short answer
Sigma isn’t a warehouse. It’s the engineering on top of one.
Sigma doesn’t compete with Snowflake or Databricks. They provide storage and compute; you still need someone to build and maintain the pipelines, models and business logic on top. Sigma does that engineering. It runs on your existing Snowflake or Databricks, using your sign-in and existing permissions, or on Sigma Cloud, a managed lakehouse on open tables if you don’t have a warehouse yet.
From Snowflake’s docs
“Cortex Agents does not join tables unless the relationships are explicitly defined in the semantic view.”
FAQ
Questions, answered.
Do I need to move my data?
No. Sigma works where your data already is, inside the permissions you already have.
What if I don’t have a warehouse?
Start on Sigma Cloud, a managed lakehouse on open tables. You can connect Snowflake or Databricks later.
