Cloud-Based Data Connections
Data connections connect your LiveRamp Clean Room organization to your data at your cloud provider so that it can be accessed in a clean room. This allows the data to be queried for lists and reports within clean rooms.
Data connections can be configured to any cloud-based storage or data warehouse location, including AWS, GCS, Azure Blob, Snowflake, Google BigQuery, and Databricks. Connections are specific to both the cloud provider and the clean room type, meaning the exact configuration will depend on source storage location, data types, and structures. For example, Snowflake data connections must be configured differently for use in hybrid clean rooms versus Snowflake clean rooms, based on whether the data being used lives in different clouds and/or cloud regions.
Note
Your Clean Room representative will work with you to determine the type(s) of data connections you’ll need for your situation.
Once you've determined the type of data connections you'll need (based on your data source and preferred configuration type), select the appropriate article to see specific configuration steps.
Each data connection results in a single dataset within LiveRamp Clean Room. All data files in a data connection job must have the same schema in order to successfully process.
To enable distinct tables or sets of files as datasets, you will need a data connection for each table or set of files.
Data Connection Prerequisites
Before creating a new data connection, you might want to have the desired data prepared and present in your cloud location. This can help speed up the connection to the data.
When creating a data connection, you will either need to utilize existing credentials that you’ve created previously for that cloud provider or you’ll need to add a new credential during the process.
Next Steps After Connecting Your Data
After you’ve created the data connection and Clean Room has connected to the data in your cloud account, you will then map the fields. This is where you specify which fields can be queryable across any clean rooms, which fields contain identifiers to be used in matching, and any columns by which you wish to partition the dataset for questions.
After fields have been mapped, you’re ready to provision the resulting dataset to your desired clean rooms. Within each clean room, you’ll be able to set dataset analysis rules, exclude or include columns, filter for specific values, and set permission levels.