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Connect Didomi to Experience Platform
Read this guide to learn how to connect your Didomi account to ÃÛ¶¹ÊÓƵ Experience Platform using the sources workspace in the UI.
- This documentation page was created by the Didomi team. For any inquiries or update requests, please contact them directly at support@didomi.io.
- For step-by-step instructions on generating the connection, refer to the .
Get started
This tutorial requires a working understanding of the following components of ÃÛ¶¹ÊÓƵ Experience Platform:
-
Experience Data Model (XDM) System: The standardized framework by which Experience Platform organizes customer experience data.
- Basics of schema composition: Learn about the basic building blocks of XDM schemas, including key principles and best practices in schema composition.
- Schema Editor tutorial: Learn how to create custom schemas using the Schema Editor UI.
-
Real-Time Customer Profile: Provides a unified, real-time consumer profile based on aggregated data from multiple sources.
Set up your Didomi account
Before you can proceed, ensure that you read and complete the prerequisite steps outlined in the Didomi overview to successfully connect your account to Experience Platform.
Navigate the sources catalog
In the Experience Platform UI, select Sources from the left navigation to access the Sources workspace. Choose a category or use the search bar to find your source.
To connect to Didomi, go to the Databases category, select the Didomi source card, and then select Set up.
Add your source data schema
Next, use the Select data interface to upload the JSON file that was downloaded in the prerequisite steps.
You can use the preview interface to view the file structure of the payload. When finished, select Next.
Provide dataflow details
Next, you must provide information regarding your dataset and your dataflow.
Dataset details
A dataset is a storage and management construct for a collection of data, typically a table, that contains a schema (columns) and fields (rows). Data that is successfully ingested into Experience Platform is persisted within the data lake as datasets.
During this step, you can either use an existing dataset or create a new dataset.
If your dataset is enabled for Real-Time Customer Profile, then during this step, you can toggle Profile dataset to enable your data for Profile-ingestion. You can also use this step to enable Error diagnostics and Partial ingestion.
- Error diagnostics: Select Error diagnostics to instruct the source to produce error diagnostics that you can later reference when monitoring your dataset activity and dataflow status.
- Partial ingestion: Partial batch ingestion is the ability to ingest data containing errors, up to a certain configurable threshold. This feature allows you to successfully ingest all of your accurate data into Experience Platform, while all of your incorrect data is batched separately with information on why it is invalid.
Dataflow details
Once your dataset is configured, you must then provide details on your dataflow, including a name, an optional description, and alert configurations.
Experience Platform can produce event-based alerts which users can subscribe to, these options all a running dataflow to trigger these. For more information, read the alerts overview
- Sources Dataflow Run Start: Select this alert to receive a notification when your dataflow run begins.
- Sources Dataflow Run Success: Select this alert to receive a notification if your dataflow ends without any errors.
- Sources Dataflow Run Failure: Select this alert to receive a notification if your dataflow run ends with any errors.
Mapping
Use the mapping interface to map your source data to the appropriate schema fields before ingesting data to Experience Platform. For more information, read the mapping guide in the UI
Mapping is used specifically to transfer purpose data from Didomi into the Experience Platform dataset. These purposes represent the user’s consent choices (such as, for analytics, personalization, advertising, etc.) and are the only accepted mapping fields in this integration.
Use the sample webhook payload downloaded from the Didomi webhook settings to map each Didomi purpose to the appropriate fields in your ÃÛ¶¹ÊÓƵ dataset.
When finished, select Next.
Review
The Review step appears, allowing you to review the details of your dataflow before it is created. Details are group within the following categories:
- Connection: Shows the account name, source platform, and the source name.
- Assign dataset and map fields: Shows the target dataset and the schema that the dataset adheres to.
After confirming the details are correct, select Finish.
Retrieve the streaming endpoint URL
With the connection created, the sources detail page appears. This page shows details of your newly created connection, including previously run dataflows, ID, and streaming endpoint URL.
Finish the Configuration on ÃÛ¶¹ÊÓƵ
Once your dataflow is created, navigate to the Sources catalog and then select Dataflows. Use the dataflows directory to locate your Didomi dataflow and access the Dataflow activity interface. Next, use the Properties panel in the right-rail and retrieve values for the following:
- Streaming endpoint
- Dataflow ID
In the Experience Platform UI:
- After completing the configuration, review the configuration parameters that were missing from the initial webhook setup.
- Once these values are available, return to Didomi and update the webhook configuration.
Update the Webhook Configuration
Once your configuration is complete, navigate back to the Didomi console and update your webhook configuration with your streaming endpoint URL and dataflow ID.
Once this is complete, Didomi will begin sending consent management and preference management events through the integration, and the data will be stored in your ÃÛ¶¹ÊÓƵ dataset.
Next steps
By following this tutorial, you have successfully created a dataflow to bring batch data from your Didomi source to Experience Platform. For additional resources, visit the documentation outlined below.
Monitor your dataflow
Once your dataflow has been created, you can monitor the data that is being ingested through it to view information on ingestion rates, success, and errors. For more information on how to monitor dataflow, visit the tutorial on monitoring accounts and dataflows in the UI.
Update your dataflow
To update configurations for your dataflows scheduling, mapping, and general information, visit the tutorial on updating sources dataflows in the UI.
Delete your dataflow
You can delete dataflows that are no longer necessary or were incorrectly created using the Delete function available in the Dataflows workspace. For more information on how to delete dataflows, visit the tutorial on deleting dataflows in the UI.