Schema Mappings

What is a Schema Mapping?

A Schema Mapping transforms data from one schema (source) to another schema (target). Schema mappings are essential when integrating systems that represent the same information differently—with different field names, data types, or structures.

Without mappings, you would need to manually transform data or write custom code for every integration. Schema mappings provide a reusable, visual way to handle these transformations.

Example

When a connector returns customer data with fields like FirstName and LastName, but your target system expects GivenName and FamilyName, a schema mapping automatically handles this conversion.

Mapping Tasks

Schema mappings consist of tasks that define how each field is transformed. Tasks execute sequentially in order, and later tasks can use outputs from earlier tasks.

Map Field

Copies data from a source field to a target field with automatic type conversion.

Setting	Description
Source Field	The field to read from the input data
Target Field	The field to write to in the output data

Use this for renaming fields or repositioning data between schemas.

Provide Value

Injects a constant value into a target field that doesn't exist in the source data.

Setting	Description
Target Field	The field to write to in the output data
Value	The constant value to use

Use this for adding default values, metadata, or system-specific information (e.g., always setting a DataSource field to a specific value).

Expression

Executes code against input data to create calculated or transformed fields.

Setting	Description
Target Field	The field to write to in the output data
Expression	The code that produces the value

Use this for complex transformations like concatenating fields, applying business logic, or performing calculations.

Task Execution

Mapping tasks execute in the order they are defined. Each task can:

• Read from the original source data
• Read outputs from previous tasks
• Write to the target schema

Only explicitly mapped fields appear in the output—fields without mappings are not automatically copied.

Using Schema Mappings in Workflow Steps

Schema mappings integrate with workflow steps at several points in the data flow.

Consume Feed

When a workflow step consumes datapoints from a feed, you can apply a schema mapping to transform the incoming data before processing.

Connector Transformation

When using connectors in a workflow step, you can apply schema mappings to transform data:

• Input: Transform data before sending it to the connector to match the schema it expects
• Output: Transform the connector's response before it's used by the workflow step

Produce Feed

When a workflow step produces datapoints to a feed, you can apply a schema mapping to transform the output data before it's written to the feed.

Reusability

A single schema mapping can be used across multiple workflow steps and connectors. This allows you to:

• Define transformations once and reuse them
• Normalize data from multiple sources into a unified schema
• Update mappings in one place to affect all workflows using them