Data Graph

The Data Graph acts as a semantic layer that allows businesses to define relationships between various entity datasets in the warehouse — such as accounts, subscriptions, households, and products — with the Segment Profile. It makes these relational datasets easily accessible to business teams for targeted and personalized customer engagements.

• Linked Audiences: Empowers marketers to effortlessly create targeted audiences by combining behavioral data from the Segment Profile and warehouse entity data within a self-serve, no-code interface. This tool accelerates audience creation, enabling precise targeting, enhanced customer personalization, and optimized marketing spend without the need for constant data team support.
• Linked Events: Allows data teams to enrich event streams in real time using datasets from data warehouses or lakes, and send these enriched events to any destination. Linked Events is available for both Destination Actions and Functions.

Prerequisites

To use the Data Graph, you’ll need the following:

• A supported data warehouse with the appropriate Data Graph permissions
• Workspace Owner or Unify Read-only/Admin and Entities Admin permissions
• For Linked Audiences, set up Profiles Sync in a Unify space with ready-to-use data models and tables in your warehouse. When setting up selective sync, Segment recommends the following settings:
  • Under Profile materialized tables, select all the tables (user_identifier, user_traits, profile_merges) for faster and more cost-efficient Linked Audiences computations in your data warehouse.
  • Under Track event tables, select Sync all Track Call Tables to enable filtering on event history for Linked Audiences conditions.

Step 1: Set up Data Graph permissions in your data warehouse

To get started with the Data Graph, set up the required permissions in your warehouse. Segment supports the following:

• Linked Audiences: Snowflake and Databricks
• Linked Events: Snowflake, Databricks, BigQuery, and Redshift

To track the data sent to Segment on previous syncs, Segment uses Reverse ETL infrastructure to store diffs in tables within a dedicated schema called _segment_reverse_etl in your data warehouse. You can choose which database or project in your warehouse this data lives in.

Step 2: Connect your warehouse to the Data Graph

To connect your warehouse to the Data Graph:

1. Navigate to Unify > Data Graph. This should be a Unify space with Profiles Sync already set up.
2. Click Add warehouse.
3. Select your warehouse type.
4. Enter your warehouse credentials.
5. Test your connection, then click Save.

Step 3: Build your Data Graph

The Data Graph is a semantic layer that represents a subset of relevant business data that marketers and business stakeholders can use for audience targeting and personalization in downstream tools. Use the configuration language spec and the following features to build your Data Graph:

• Use the Warehouse access tab to view the warehouse tables you’ve granted Segment access to
• Begin typing to autopopulate the configuration spec within the editor, as well as to autocomplete your warehouse schema
• Validate your Data Graph using the Preview tab

Key steps to build your Data Graph

1. First, define your entities. An entity corresponds to a table in your warehouse. Segment flexibly supports tables, views and materialized views.
2. Then, define the profile block. This is a special class of entity that represents Segment Profiles, which corresponds to the Profiles Sync tables and models. For Linked Audiences, this allows marketers to filter on profile traits, event history, and so on.
3. Finally, define how your datasets are related to each other. The Data Graph preserves these relationships and carries this rich context to the destinations to unlock personalization.

Defining Relationships

Similar to the concept of cardinality in data modeling, the Data Graph supports 3 types of relationships:

• Profile-to-entity relationship: This is a relationship between your entity table and the Segment Profiles tables, and is the first level of relationship.
• 1:many relationship: For example, an account can have many carts, but each cart can only be associated with one account.
• many:many relationship: For example, a user can have many carts, and each cart can have many products. However, these products can also belong to many carts.
• The Data Graph currently supports 6 levels of depth (or nodes) starting from the profile. For example, relating the profile to the accounts table to the carts table is 3 levels of depth. There are no limits on the width of your Data Graph or the number of entities.
• Relationships are nested under the profile. Refer to the example below.

Data Graph Example

data_graph {
    version =  "v1.0.0"
  
    # Define entities
    entity "account-entity" {
      name = "account"
      table_ref = "PRODUCTION.CUST.ACCOUNT"
      primary_key = "ID"
    }
  
    entity "product-entity" {
      name = "product"
      table_ref = "PRODUCTION.PROD.PRODUCT_SKUS"
      primary_key = "SKU"
    }
  
    entity "cart-entity" {
      name = "cart"
      table_ref = "PRODUCTION.CUST.CART"
      primary_key = "ID"
      enrichment_enabled = true
    }

    entity "household-entity" {
      name = "household"
      table_ref = "PRODUCTION.CUST.HOUSEHOLD"
      primary_key = "HOUSEHOLD_ID"
    }

    entity "subscription-entity" {
      name = "subscription"
      table_ref = "PRODUCTION.CUST.SUBSCRIPTION"
      primary_key = "SUB_ID"
    }
  
    # Define the profile entity, which corresponds to Segment Profiles tables synced via Profiles Sync
    # Recommend setting up Profiles Sync materialized views to optimize warehouse compute costs
    profile {
      profile_folder = "PRODUCTION.SEGMENT"
      type = "segment:materialized"
  
      # First branch - relate accounts table to the profile
      # This is a unique type of relationship between an entity and the profile block
      relationship "user-accounts" {
        name = "Premium Accounts"
        related_entity = "account-entity"
        # Join the profile entity with an identifier (e.g. email) on the related entity table
        # Option to replace with the traits block below to join with a profile trait on the entity table instead
        external_id {
          type = "email"
          join_key = "EMAIL_ID"
        }
  
        # Define 1:many relationship between accounts and carts
        # e.g. an account can be associated with many carts
        relationship "user-carts" {
          name = "Shopping Carts"
          related_entity = "cart-entity"
          join_on = "account-entity.ID = cart-entity.ACCOUNT_ID"
    
          # Define many:many relationship between carts and products
          # e.g. there can be multiple carts, and each cart can be associated with multiple products
          relationship "products" { 
            name = "Purchased Products"
            related_entity = "product-entity"
            junction_table {
              primary_key = "ID"
              table_ref = "PRODUCTION.CUSTOMER.CART_PRODUCT"
              left_join_on = "cart-entity.ID = CART_ID"
              right_join_on = "PRODUCT_ID = product-entity.SKU"
            }      
          }
        }
      }

      # Second branch - relate households table to the profile by joining with an external ID block
      relationship "user-households" {
        name = "Households"
        related_entity = "household-entity"
        external_id {
          type = "email"
          join_key = "EMAIL_ID"
        }
  
        # Define 1:many relationship between households and subscriptions
        # e.g. a household can be associated with multiple subscriptions
        relationship "user-subscriptions" {
          name = "Subscriptions"
          related_entity = "subscription-entity"
          join_on = "household-entity.SUB_ID = subscription-entity.HOUSEHOLD_ID"
    }
}

3a: Define entities

The first step in creating a Data Graph is to define your entities. An entity corresponds to a table in the warehouse.

Example:

data_graph {
    entity "account-entity" {
      name = "account"
      table_ref = "PRODUCTION.CUST.ACCOUNT"
      primary_key = "ID"
    }
    
    entity "cart-entity" {
      name = "cart"
      table_ref = "PRODUCTION.CUST.CART"
      primary_key = "ID"
      enrichment_enabled = true
    }
}

3b: Define the profile

Next, define the profile. This is a special class of entity that represents Segment Profiles, which corresponds to the Profiles Sync tables and models. For Linked Audiences, this allows marketers to filter on profile traits, event history, etc. There can only be one profile for a Data Graph.

Example:

data_graph {
    # Define entities
    ...
  
    # Define the profile entity, which corresponds to Segment Profiles tables synced via Profiles Sync
    # Recommend setting up Profiles Sync materialized views to optimize warehouse compute costs
    profile {
      profile_folder = "PRODUCTION.SEGMENT"
      type = "segment:materialized"
    }
}

3c: Define relationships

Now define your relationships between your entities. Similar to the concept of cardinality in data modeling, the Data Graph supports 3 types of relationships below. All relationship types require you to define the relationship slug, name, and related entity. Each type of relationship has unique join on conditions.

• Profile-to-entity relationship: This is a relationship between your entity table and the Segment Profiles tables, and is the first level of relationship.
• 1:many relationship: For example, an account can have many carts, but each cart can only be associated with one account.
• many:many relationship: For example, a user can have many carts, and each cart can have many products. However, these products can also belong to many carts.

Define profile-to-entity relationship

This is the first level of relationships and a unique type of relationship between the Segment profile entity and a related entity.

To define a profile-to-entity relationship, reference your entity table and depending on your table columns, choose to join on one of the following:

Option 1 (Most common) - Join on an external ID: Use the external_id block to join the profile entity with an entity table using external IDs from your Unify ID resolution settings. Typically these identifiers are user_id, email, or phone depending on the column in the entity table that you want to join with.

• type: Represents the external ID type (email, phone, user_id) in your id-res settings. Depending on if you are using materialized or unmaterialized profiles, these correspond to different columns in your Profiles Sync warehouse tables:
  • Materialized (Recommended): This corresponds to the type column in your Profiles Sync user_identifiers table.
  • Unmaterialized: This corresponds to the external_id_type column in your Profiles Sync external_id_mapping_updates table.
• join_key: This is the column on the entity table that you are matching to the external identifier.

Option 2 - Join on a profile trait: Use the traits block to join the profile entity with an entity table using Profile Traits.

• name: Represents a trait name in your Unify profiles. Depending on if you are using materialized or unmaterialized profiles, these correspond to different columns in your Profiles Sync warehouse tables:
  • Materialized (Recommended): The trait name corresponds to a unique value of the name column in your Profiles Sync user_traits table.
  • Unmaterialized: This corresponds to a column in the Profile Sync profile_trait_updates table.
• join_key: This is the column on the entity table that you are matching to the trait.

Example:

data_graph { 
    entity "account-entity" {
      name = "account"
      table_ref = "PRODUCTION.CUST.ACCOUNT"
      primary_key = "ID"
    }
  
    # Define additional entities...

    # Note: Relationships are nested
    profile {
      profile_folder = "PRODUCTION.SEGMENT"
      type = "segment:materialized"
  
      # Relate accounts table to the profile 
      relationship "user-accounts" {
        name = "Premium Accounts"
        related_entity = "account-entity"
  
        # Option 1: Join the profile entity with an identifier (e.g. email) on the related entity table
        external_id {
          type = "email"
          join_key = "EMAIL_ID"
        }
  
        # Option 2: Join the profile entity with a profile trait on the related entity table
        trait {
          name = "cust_id"
          join_key = "ID"
        }
      }
    }
}

Define a 1:many relationship

For 1:many relationships, define the join on between the two entity tables using the spec below.

Example:

data_graph { 
    entity "cart-entity" {
      name = "cart"
      table_ref = "PRODUCTION.CUST.CART"
      primary_key = "ID"
    }
  
   # Define additional entities...

    # Note: Relationships are nested
    profile {
      profile_folder = "PRODUCTION.SEGMENT"
      type = "segment:materialized"
                   
      relationship "user-accounts" {
        ...
  
        # Define 1:many relationship between accounts and carts
        relationship "user-carts" {
          name = "Shopping Carts"
          related_entity = "carts-entity"
          join_on = "account-entity.ID = cart-entity.ACCOUNT_ID"
        }
      }
    }
}

Define many:many relationship

For many:many relationships, define the join on between the two entity tables with the junction_table.

Junction table spec

Example:

data_graph { 
    # Define entities

    # Note: Relationships are nested
    profile {
      # Define profile
         
      relationship "user-accounts" {
        ...
  
        relationship "user-carts" {
          ...
  
          # Define many:many relationship between carts and products
          relationship "products" {
            name = "Purchased Products"
            related_entity = "product-entity"
            junction_table {
              table_ref = "PRODUCTION.CUSTOMER.CART_PRODUCT"
              primary_key = "ID"
              left_join_on = "cart-entity.ID = CART_ID"
              right_join_on = "PRODUCT_ID = product-entity.SKU"
            }
          }
        }
      }
  }
}
         

Step 4: Validate your Data Graph

You can validate your Data Graph using the preview, then click Save. After you’ve set up your Data Graph, your partner teams can start leveraging these datasets with with Linked Events and Linked Audiences.

Edit and manage your Data Graph

To edit your Data Graph:

1. Navigate to Unify > Data Graph.
2. Select the Overview tab, and click Edit Data Graph.

View Data Graph data consumers

A data consumer refers to a Segment feature like Linked Events and Linked Audiences that are referencing datasets, such as entities and/or relationships, from the Data Graph. You can view a list of data consumers in two places:

• Under Unify > Data Graph, click the Data consumers tab
• Under Unify > Data Graph > Overview or the Data Graph editor > Preview, click into a node on the Data Graph preview and a side sheet will pop up with the list of data consumers for the respective relationship

Understand changes that may cause breaking and potential breaking changes

Upon editing and saving changes to your Data Graph, a modal will pop up to warn of breaking and/or potential breaking changes to your data consumers. You must acknowledge and click Confirm and save in order to proceed.

• Definite breaking change: Occurs when deleting an entity or relationship that is being referenced by a data consumer. Data consumers affected by breaking changes will fail on the next run. Note: The entity and relationship slug are immutable and treated as a delete if you make changes. You can modify the label.
• Potential breaking change: Some changes such as updating the entity table_ref or primary_key, may lead to errors with data consumers. If there’s a breaking change, the data consumer will fail on the next run. Unaffected data consumers will continue to work.

Detect warehouse breaking changes

Segment has a service that regularly scans and monitors the Data Graph for changes that occur in your warehouse that may break components of the Data Graph, such as when the table being referenced by the Data Graph gets deleted from your warehouse or when the primary key column no longer exists. An alert banner will be displayed on the Data Graph landing page. The banner will be removed once the issues are resolved in your warehouse and/or the Data Graph. You will also have the option to trigger a manual sync of your warehouse schema.