Unified attributes are derived attributes that you populate by mapping one or more attributes from your input datasets into a single attribute in the schema for the unified dataset.
- Each unified attribute corresponds to the header of one column in the unified dataset.
- A schema is a collection of unified attributes.
- You can map more than one attribute from an input dataset to the same unified attribute.
- The mappings can be one-to-one or many-to-one.
- You can choose to ignore attributes in input datasets and not map them to any unified attributes.
- You can add unified attributes and populate them with the results of transformations instead of mapping input attributes to them directly.
To create a schema, also known as the unified schema, for the unified dataset, you can:
- Design a set of unified attributes ahead of time and add them to the unified schema manually. You can then map attributes from the input datasets to these unified attributes.
- "Bootstrap" unified attributes from specified attributes in an input dataset. Bootstrapping performs these steps:
- Creates a unified attribute with the same name as the input attribute.
- Maps the selected input attributes to the unified attributes.
If multiple input attributes that you select have the same name (for example, from different input datasets), bootstrapping creates a single unified attribute with that name and maps all of those input attributes to it.
Tip: If you intend to include numerous or complex data transformations in your project, consider using a naming convention like "_original" for a set of unified attributes that will never be modified by transformations, and map input attributes to those "original" attributes. You can then add other attributes to your unified schema to populate with the results of transformations. This approach can help you maintain a very clear data lineage.
Creating a unified schema is often an iterative process, especially as you add new input datasets over time. For example, as you work with your data, you may find that you would like to add more attributes from other input datasets to help describe a particular entity. Tamr helps automate most of the schema mapping process.
Updated 3 months ago
After you have created the basic unified schema, run Tamr machine learning to improve your schema. Or, if the schema is fully mapped (as it may be for a categorization or mastering project), you can proceed to formatting the schema for the project.
|Working with Unified Attributes|