Snowflake + Eru
Connect your data warehouse to map dbt models, raw tables, and transformed data to business entities. Reconcile warehouse data with live SaaS sources.
What Eru reads from Snowflake
Eru connects to Snowflake with a read-only user and explores your warehouse schema. It discovers databases, schemas, tables, and views—including dbt-created models if you use them.
Tables and views
All accessible tables and views with column metadata. Eru scans schema information and samples data to understand what each table represents and how tables relate to each other.
dbt models
If you use dbt, Eru discovers your model layer. Staging models, intermediate transformations, and final marts all become part of the Truth Map, with lineage relationships preserved.
Raw and transformed data
Both raw ingested data (from Fivetran, Airbyte, etc.) and transformed analytics tables. Eru maps how raw data flows through transformations to final business entities.
Usage and query patterns
Optional access to query history helps Eru understand which tables are actually used, by whom, and for what purposes. This informs mapping confidence.
Warehouse + SaaS reconciliation
Your warehouse often contains data that also lives in SaaS tools. This creates reconciliation challenges and opportunities.
Source of truth questions
Is the warehouse or the SaaS system authoritative? Often it depends on the use case. Eru helps you define and document these decisions explicitly.
Freshness comparison
Warehouse data might lag behind live SaaS data by hours or days. Eru tracks freshness and factors it into reconciliation checks.
Transformation verification
When you transform data in dbt, you're making assumptions. Eru can verify that transformed data matches source system logic, catching when transformations drift.
Common use cases
Warehouse-to-source reconciliation
Verify that data flowing into Snowflake matches source systems. Catch ingestion failures, schema changes, and transformation bugs.
dbt model validation
Ensure dbt transformations produce expected results. Compare model outputs against source system logic.
Cross-system entity mapping
Link warehouse customer tables to CRM accounts, billing customers, and product users. Build a unified entity layer across all data.
Metric consistency
Verify that metrics calculated in the warehouse match metrics from source systems. MRR in Snowflake should equal MRR in Stripe.
Setup
1. Create a read-only user
Create a Snowflake user with SELECT access to relevant databases and schemas. Eru never needs write permissions.
2. Connect in Eru
Enter connection details through Eru's secure credential form. Account identifier, warehouse, database, and credentials are stored encrypted.
3. Eru explores your schema
The agent scans available tables, samples data, and builds a model of your warehouse structure. It identifies potential entity tables and relationship patterns.
4. Map to business entities
Eru proposes mappings between warehouse tables and business concepts. Your dim_customers table maps to the Customer entity, linked to CRM and billing representations.
Also works with
Eru supports other SQL-based data warehouses and databases:
Churn prediction guide
If you use Snowflake with dbt and want to set up cross-system churn prediction, see our detailed technical guide covering authentication, schema mapping, and dbt model compatibility.
Other integrations
Connect Snowflake to Eru
Unify your warehouse with live SaaS data.