Tamr Documentation

Unified Attributes

Attributes from multiple input datasets are mapped to "unified" attributes in your target dataset.

Unified attributes are derived attributes that you create by mapping one or more attributes from your input datasets into a single attribute in the schema for the unified dataset.

  • Each unified attribute represents the header for one column in the unified dataset.
  • A schema is a collection of unified attributes.
  • The mappings can be one-to-one or many-to-one.
  • You can map more than one attribute from an input dataset to the same unified attribute.
  • You can choose to ignore attributes in input datasets and not map them to any unified attributes.

Approaches to Creating a Unified Schema

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 then map attributes from the input datasets to these unified attributes.
  • "Bootstrap" a unified schema from an input dataset. Bootstrapping performs these steps:
    • Creates a unified attribute with the same name as the input attribute. You have the option t rename the unified attribute.
    • Groups attributes with the same name together.
    • Automates mapping assignments of input attributes to unified attributes.

Tip: If you intend to include numerous or complex data transformations in your project, consider using a naming convention like "<name>_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. For example, as you work with your data, you may find that you would like to add more attributes from new input datasets to help describe a particular entity. Tamr helps automate most of the schema mapping process.

Updated 3 months ago


What's Next

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
Transformations

Unified Attributes


Attributes from multiple input datasets are mapped to "unified" attributes in your target dataset.

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