Tamr Documentation


Transformations are currently in BETA for categorization and mastering projects.

Transformations are an incredibly useful tool to affect the final output of a unified dataset without having to individually transform each of the sources before ingesting them into Tamr. Transformations can be applied to all records in the unified dataset, or records from specific input datasets (but will not change the input datasets themselves). Transformations will not alter input datasets directly. Transformations can be accessed from the Unified Dataset tab.

Transformations operates on one dataset at a time and each transformation produces a new dataset from the current dataset.

Accessing Transformations

On the Unified Dataset page, there is a small arrow on the right hand side, and a large button reading Transformation Steps. Clicking either of these will display a pull-out menu, where transformations can be added.

To begin adding transformations, simply click the Add Transformation button.

Changing Scope

Transformations can be applied to records from specific input datasets, or to records from the unified dataset after the input datasets have been unioned. You can collapse or expand the two sections, and add a transformation directly to each section. You may also drag a transformation across the open sections to change the scope. If you add a transformation in the Input Datasets section, you will see "For records from __ datasets" in the bottom left corner. Clicking on this text allows you to choose which datasets will have transformations applied to their records. One or more datasets can be chosen.

Displaying Transformations

Once a transformation is written, you can use the Preview all button in the transformations panel to view a preview of the transformation. If the transformation violates any rules, you will receive an error message explaining why it failed. This preview is a way to test transformations without affecting the entire dataset; you can iterate and improve your transformations quickly, viewing the results as you go before you save any changes.

Once you are satisfied with the results, you can save the changes. The save button will have a small number on it, indicating how many changes have been made since the last save. If transformations are not saved, they will not persist if you navigate away from the page. The save button will be disabled if you have any transformations with errors.

Clicking Cancel changes will revert your transformations back to the last time they were saved, thereby undoing all changes noted in the orange badge on the save button.

You can also preview through a set of transformations by clicking Preview on each individual transformation. Subsequent transformations will be grayed out to signify that they are not included in the preview, but can still be edited and reordered. Pressing Save changes will save all changes, not only those made to the previewed set of transformations.

Reordering Transformations

Transformations only have local effects and can be reordered freely. This reordering may change the output, but are permitted at any time.

Transformations can be reordered by clicking and holding on the two horizontal line icon at the top left of the transformation and then dragging it to the desired location.

Saving and Applying Transformations

Applying Transformations

In order for transformations to be saved as part of the data pipeline, you must use the Update Unified Dataset button found on the Unified Dataset page and Schema Mapping page to apply the transformations to the unified dataset.

The Save changes button on the transformation panel keeps your work in case you navigate away or want to come back to it later; to apply this saved work to all of your records in your Unified Dataset, export the transformed Unified Dataset, or have the transformed Unified Dataset continue in your data pipeline, make sure to use the Update Unified Dataset button.

Data Types

Unify Transformations supports multiple data types. The data types are

Data type
Literal representation













Some functions accept values of any data type, denoted as type any. You can convert between data types using casting functions such as to_integer(), which casts values of any type to type integer. Any data that fails to convert will return a null. Some functions only accept values of certain data types. For example, upper() can only be used on data with a type string.

The complex data type array<> supports each primitive data type, e.g. array<integer>. Additionally it supports nesting, e.g. array<array<string>>.

The Unify-generated attributes, origin_entity_id, origin_source_name, and tamr_id, have a default data type of string and must be of type string when used in the unified dataset. Non Unify-generated attributes have a default data type of array<string> and may be of any type when used in the unified dataset.

Additional Information

Accessible Functions

A list of all supported functions is maintained here.

Referencing Attributes

Attributes can be referenced by wrapping them in double quotes, although this is not required (attribute and "attribute" both work). You may reference an attribute without using any quotes, however, any attribute containing spaces or escaped characters must be wrapped in double quotes. An attribute name containing double quotes itself can be referenced by escaping the double quotes. For example, this is an "attribute name" becomes "this is an ""attribute name""".

When writing attributes in double quotes, keep in mind that they are case sensitive.

Referencing Datasets

Dataset names follow the same pattern, and need to be wrapped in double quotes if they include spaces or escaped characters , e.g. USE "myData.csv"; or USE my_data;. See join for an example referencing a source dataset.

Using Single Quotes

Single quotes are interpreted as string literals 'string'.

Tab Autocomplete

For transformations such as Script and Formula, pressing the 'tab' key will give a list of suggested input, including functions and attributes.

What's Next