Graph Studio runs on both Snowflake and AWS data planes. For a conceptual overview, see Graph Studio.
Prerequisites
- A dataset with Rosetta Stone attribute mappings for a unique identifier and at least one identity attribute (e.g., normalized email, phone number)
- The dataset must be eligible as a Graph Studio source — the Edge Builder only lists datasets tagged for Graph Studio, and the Graph Builder only lists edges datasets. See Source eligibility if a source is missing from a picker.
- A Snowflake or AWS data plane
Example dataset
This guide uses a CRM dataset calledOFFICE_CRM:
Michael Scott appears three times with different email and phone combinations. The goal is to resolve all three records into a single identity.
The dataset is mapped to Rosetta Stone attributes for Unique Identifier (using
CUSTOMER_ID), Normalized Email, Clear Text E.164 Phone Number, and Person Name.
Step 1: Build edges
Edges define how records connect to each other through shared identifiers. Navigate to My Data > Graph Studio and select the Edge builder tab.1
Add a source dataset
Click Select Sources and choose your dataset. Set a source ID type (a label like
OFFICE_CRM that identifies this system) and choose the source ID field (CUSTOMER_ID).2
Choose target IDs
Target IDs are the Rosetta Stone attributes that serve as connection points. When two records share the same target ID value, the graph connects them.Target IDs are grouped — each group acts as a single connection type. For this example, add two target ID groups:
- Normalized Email — connects records that share the same email address
- Clear Text E.164 Phone Number + Person Name > first_name — connects records that share both the same phone number and first name. Combining these into one group means both values must match for a connection, which is more precise than matching on phone alone.
3
Finalize and build
Name the edge dataset (e.g.,
office_crm_edges) and click Build Edges.Step 2: Build the graph
Switch to the Graph builder tab. This takes your edges and runs a connected components algorithm to discover which records belong to the same person.1
Select input sources
Click Select Sources and choose the edges dataset you just created.
2
Review algorithm parameters
The defaults work well for most use cases. You can adjust max component size (caps how many records can merge into one identity), max iterations, and max degree threshold (excludes overly-connected nodes like shared corporate emails) if needed later.Optionally, use Exclusive attributes to pick one or more target ID types that must hold a single value per identity (for example, an SSN token or exact date of birth). After the connected-components pass converges, any component whose vertices disagree on a declared attribute is split apart, targeting known overmerge cases without dropping legitimate connections. Only first-party target ID types present on your input edges are selectable; leaving the field empty preserves the default behavior.
3
Finalize and build
Name the graph (e.g.,
office_crm_graph), choose a refresh schedule, and click Build Graph +.Results
The graph resolves the six CRM records into four identities:
Michael Scott’s three records are resolved into a single identity even though no two records share both the same email and phone — the graph follows transitive connections across shared values to link them together.
Dwight, Jim, and Pam remain as separate identities because they have no overlapping identifiers with other records in this dataset.
Next steps
- Add third-party data — Include an access rule as an additional source in the Edge Builder to introduce connections your first-party data cannot see on its own. See Graph Enrichment.
- Set up recurring builds — Use a refresh schedule to keep your graph current as new data arrives.