You can apply transformations to all records in the unified dataset, or to records from specific input datasets. Transformations create new datasets and do not change the input datasets themselves. Locate transformations on the Unified Dataset tab. Transformations operate on one dataset at a time and each transformation produces a new dataset from the current dataset.
To access transformations:
- On the Unified Dataset page, select Show Transformations. A pull-out menu displays where you can start adding transformations.
- Choose Add Transformation.
You can apply transformations to records from specific input datasets, or to records from the unified dataset after the input datasets have been unioned. When the input datasets are unioned, the attributes are converted to the data type associated with that unified attribute (default of array[string]).
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 its scope. If you add a transformation in the Input Datasets section, Tamr reflects this inside a note "For records from __ datasets" in the bottom left corner of the transformations panel. Selecting text allows you to choose which datasets will have transformations applied to their records. You can also choose additional datasets.
Once you write your transformation, you can use Preview all in the transformations panel to preview a transformation. If the transformation violates any rules, Tamr issues an error message explaining why it failed. This preview allows you to test transformations without affecting the entire dataset. You can iterate and improve your transformations quickly, viewing the results as you go before saving any changes.
Once you are satisfied with the transformation results, save the changes. The number on the Save button indicates how many changes have been made since the last time you saved changes. If you don't save transformations, they will not persist if you navigate away from the page. The Save button is disabled if you have any transformations with errors and when no transformations have changed.
To revert transformations and go back to the last time they were saved, choose Cancel Changes, This reversts all changes that weren't saved.
You can also preview a set of transformations by clicking Preview on each individual transformation. Subsequent transformations are then grayed out to signify that they are not included in the preview, but you can still edit and reorder them. To save changes, select *Save changes. This saves all changes, and not only those made to the previewed set of transformations.
To preview your data before any transformations are applied, choose Preview at the top of the Input Datasets section.
Transformations only have local effects and you can reorder them at any time. Reordering may change the output.
To reorder transformations, select and hold the icon with two horizontal lines at the top left of the transformations panel and then drag it to the desired location within the transformations script.
To apply transformations so that they become part of the data pipeline for your unified dataset, choose Update Unified Dataset on the Unified Dataset and Schema Mapping pages. This applies 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 records in the unified dataset, export the transformed unified dataset. To include the transformed unified dataset in your data pipeline, select Update Unified Dataset.
The primary key of a dataset is a specific minimal set of attributes that uniquely identify a record.
For example, when you upload a source dataset to Tamr, Tamr selects the dataset's primary key as the dataset column that uniquely identifies its records. If no such column exists, Tamr adds a column and populates it using a generated value that is guaranteed to be unique. See Uploading a Dataset.
All non-input (or non-source) datasets in Tamr are known as derived datasets. Derived datasets also have a primary key column.
Unified datasets, for example in a Mastering or Categorization project, may be derived from one or more input datasets. In such cases, Tamr automatically generates the additional column
tamr_id to uniquely identify the records of the unified dataset.
When working with transformations, to manipulate input of derived datasets, the creation and populating of the column
tamr_id can be automatic or manual:
- The automatic generation and populating of
tamr_idensures that records are always uniquely identified. It is a convenient feature when working with transformations such as
JOIN, that transform the uniqueness of records. Automatic primary key management was introduced in Tamr v.2019.014.1.
- The manual generation and populating of
tamr_idallows you to not only ensure that records are always uniquely identified, but also, in contrast to the Tamr-generated values, consider the stability of the values in uniquely identifying the same record over time.
User feedback is linked to tamr_id
All types of user feedback, including record categorizations, record pair labels, record locks, and record comments, are linked to the
tamr_id of the unified dataset of the project. If you add or change transformations that change the value of
tamr_id, user feedback, such as labels, will be lost.
If you have started using Tamr after v.2019.014.1, Tamr automatically manages primary keys and you don't need to turn this feature off, for any projects, unless you have a specific workflow that would require you to specify your own keys.
If you have created projects before Tamr v.2019.014.1, then you may want to temporarily disable automatic assignment of primary keys for workflow stability between versions. For example, this might be useful if you don't want to lose your labels after an upgrade. In some cases, you may also want to always create primary keys manually. In this case, you can disable automatic management of keys using the methods listed in the following procedure.
You can add
USE HINT statements manually or with a script.
- Manual option. The
USE HINTstatements apply a hint to the current transformation in the editor and to all of the subsequent transformations in that project.
- Script option. An option in the
<unify-zip>/tamr/libs/transform-tools.jarscript exists to automate the process of disabling primary key assigments after an upgrade.
To manually manage the primary key
- Use transformations to directly populate the attribute
tamr_id, and use one of these methods:
a. Use the transformations
HINT, and specify
pkmanagement.manual. For example, to disable automatic primary key management by Tamr in a particular project, add:
USE HINT(pkmanagement.manual);in the first transformation, or
b. Use an option from
<unify-zip>/tamr/libs/transform-tools.jarscript. This option adds a
HINTto project's transformations. To learn how to use it, run
java -jar transform-tools.jaror
java -jar transform-tools.jar pk-mgmt-disabler.
Transformations in Tamr support multiple data types. The data types are:
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 returns a null. Some functions only accept values of certain data types. For example, you can use
upper() only on data with a type
The complex data type
array[ ] supports each primitive data type, such as
array[integer]. It also supports nesting of datatypes, for example:
To view the data type of a unified attribute at that point within a transformation script, hover over the name of a unified attribute in the script.
Those attributes that are generated by Tamr, such as
tamr_id, have a default data type of
string and must be of type
string when you include them in the unified dataset. Attributes that aren't generated by Tamr have a default data type of
array[string] and may be of any type when used in the unified dataset.
See also Geospatial Data Types.
You can enable transformations in Categorization and Mastering projects during project creation, or after a project is already created by choosing the pencil icon on the project card. Once enabled, transformations cannot be disabled.
If you are writing transformations in a Categorization or Mastering project, or plan to use a unified dataset containing transformations in a second project, it is important that the Tamr-generated columns
origin_entity_id meet certain conditions. Transformations can be used to maintain these conditions:
origin_source_namemust be a string type. Each string should be a name of one of the input datasets.
origin_entity_idmust be a string type.
Additionally, the column
tamr_id generated by Tamr must be a unique string type, since it is a primary key that Tamr manages for you.
For a full list of all supported functions, see column-producing functions.
To reference attributes in a transformation script, wrap them in double quotes, although this is not required (
"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""".
Attributes in transformations are case sensitive.
Dataset names follow the same pattern as attributes. Wrap dataset names in double quotes if they include spaces or escaped characters, such as
USE "myData.csv"; or
USE my_data;. See join for an example referencing an input dataset.
Single quotes are interpreted as string literals
For transformations such as Script and Formula, pressing the
tab key provides a list of suggested inputs, including functions and attributes.
Hints autocomplete with
tab in the code editor.
Updated about a month ago