Transformations

Transformations provide a way to modify, enrich, or filter dispatches before they are sent to dispatchers. Use transformations to implement custom business logic that transforms your data based on specific conditions and scopes.

How it works

A transformation runs at a defined point in the dispatch pipeline and can modify, enrich, or drop an event before it reaches a dispatcher.

A transformation’s scope determines when it runs:

• After collectors: Runs after data collection, before any dispatcher receives the event. Use this for enrichment that applies to all dispatchers.
• All dispatchers: Runs for every dispatcher.
• Specific dispatcher: Runs only for a named dispatcher. Use this for destination-specific filtering or data masking.

Each transformation can include conditions, so it only applies when specific data is present in the dispatch payload.

Common transformation patterns

Data enrichment

Add additional data to dispatches based on existing values. For example, add a user segment based on purchase amount.

Data filtering

Remove sensitive or unwanted data from dispatches before they reach dispatchers. Configure a list of sensitive keys to strip from the payload.

Conditional dispatch dropping

Drop dispatches based on specific conditions. Return nil (Swift) or null (Kotlin) from the applyTransformation method to drop a dispatch entirely. For example, drop test events in production.

Scope-specific transformations

Apply different transformations at different pipeline stages. Use afterCollectors for enrichment that applies to all dispatchers, and dispatcher(id:) for filtering specific to a single destination.

Configure transformations in the UI

Transformations are configured in your mobile profile as extensions. They work identically to tag management extensions, but include mobile-specific scopes.

To add an extension in the UI:

1. Log in to your mobile profile and go to Extensions.
2. Select Add Extension and choose an extension type.
3. Configure the extension and set its scope.
4. Save and publish your changes.

Mobile-specific scopes available in the UI:

Advanced

Configure transformations programmatically when the logic depends on values only known at runtime or when you need to guarantee a transformation runs regardless of remote settings. For example, you might dynamically register a PII-stripping transformer scoped to a specific dispatcher based on a user’s consent state, or lock in a data enrichment transformation during development to test behavior before publishing it to the platform.

Programmatic configuration

Programmatic transformation configuration is not available in React Native. Configure transformations in your Tealium mobile profile.

Scope constants for reference:

TransformationScope.AFTER_COLLECTORS
TransformationScope.ALL_DISPATCHERS
TransformationScope.dispatcher("my_dispatcher")

TransformationScope.afterCollectors
TransformationScope.allDispatchers
TransformationScope.dispatcher(id: "my_dispatcher")

Example: configure a transformation in code:

val enrichmentTransformation = TransformationSettings(
    id = "user_enrichment",
    transformerId = "DataEnrichmentTransformer",
    scopes = listOf(TransformationScope.AFTER_COLLECTORS),
    configuration = mapOf(
        "enable_enrichment" to true,
        "enrichment_type" to "user_data"
    ),
    conditions = Rule.just(Condition.isDefined("user_id"))
)

config.setTransformation(enrichmentTransformation)

let enrichmentTransformation = TransformationSettings(
    id: "user_enrichment",
    transformerId: "DataEnrichmentTransformer",
    scopes: [.afterCollectors],
    configuration: [
        "enable_enrichment": true,
        "enrichment_type": "user_data",
        "source": "user_profile"
    ],
    conditions: .just(Condition.isDefined(variable: "user_id"))
)

config.setTransformation(enrichmentTransformation)

JSON configuration

Transformations can also be defined in JSON format for remote or local configuration:

{
  "transformation_id": "user_enrichment",
  "transformer_id": "MyCustomTransformer",
  "scopes": ["aftercollectors"],
  "configuration": {
    "enable_enrichment": true,
    "enrichment_type": "user_data",
    "source": "user_profile"
  },
  "conditions": {
    "operator": "defined",
    "variable": {"key": "user_id"}
  }
}

Create a custom transformer

To create a custom transformer, implement the Transformer protocol:

class MyCustomTransformer : Transformer {
    override val id: String = "MyCustomTransformer"
    override val version: String = "1.0.0"

    override fun applyTransformation(
        transformation: TransformationSettings,
        dispatch: Dispatch,
        scope: DispatchScope,
        completion: (Dispatch?) -> Unit
    ) {
        val enableEnrichment: Boolean =
            transformation.configuration.get("enable_enrichment") ?: true

        val modifiedDispatch = dispatch.toMutableDispatch()

        if (enableEnrichment) {
            modifiedDispatch.enrich(mapOf("transformed" to "true"))
        }

        // Return the modified dispatch
        completion(modifiedDispatch)

        // Or return null to drop the dispatch entirely
        // completion(null)
    }
}

class MyCustomTransformer: Transformer {
    static let moduleType = "MyCustomTransformer"
    var id: String { Self.moduleType }
    let version: String = "1.0.0"

    func applyTransformation(_ transformation: TransformationSettings,
                           to dispatch: Dispatch,
                           scope: DispatchScope,
                           completion: @escaping (Dispatch?) -> Void) {
        let enableEnrichment: Bool =
            transformation.configuration.get(key: "enable_enrichment") ?? true

        var modifiedDispatch = dispatch

        if enableEnrichment {
            modifiedDispatch.enrich(data: ["transformed": "true"])
        }

        // Return the modified dispatch
        completion(modifiedDispatch)

        // Or return nil to drop the dispatch entirely
        // completion(nil)
    }
}