Lookup Tables

Use lookup tables to transform identity attributes for target systems

Overview

You can use Lookup transformers to convert identity attributes from a source system into appropriate values for target systems based on CSV reference tables. This is particularly useful when mapping values between systems that use different naming conventions, codes, or formats for the same conceptual data.

For example, you might need to transform a "Location" attribute from Workday (which might be stored as location codes like "MN001") into corresponding values for country, country code, or city names in a target system.

Use Table Lookup Transformers when:

• You need to map source attribute values to different values in target systems

• You have standardized reference data that must be consistent across applications

• You need to extract different pieces of information from a single attribute value

• You have complex mapping requirements that built-in transformers cannot support

Examples

1. Geographic Information:

   • Transform location codes to country, region, city, or timezone information

   • Map office codes to physical addresses or facility types

2. Organizational Mapping:

   • Convert department codes to department names or business units

   • Map cost centers to budget codes or accounting categories

3. System-Specific Configurations:

   • Transform job titles to role designations in target systems

   • Convert skill codes to certification requirements or training needs

How It Works

The Table Lookup Transformer references CSV-based mappings between source and destination values. When synchronizing user attributes, Veza:

1. Takes the source attribute value

2. Looks up this value in the specified lookup table

3. Returns the corresponding value from the designated return column

4. Applies this value to the target attribute

Lookup Table Structure

Lookup tables are CSV files with columns that map values from a source of identity to destination values. Each row represents a mapping entry. The first row must contain the column headers.

For example, a location mapping table might look like:

location_code,state_code,state,city
MN001,MN,Minnesota,Minneapolis
CA001,CA,California,Los Angeles
TX001,TX,Texas,Houston
TX002,TX,Texas,Austin

Creating and Managing Lookup Tables

Creating a Lookup Table

To create a new lookup table:

1. Navigate to the Lookup Tables tab within your policy configuration

2. Click Edit mode to enable policy changes

3. Click Add New to create a new lookup table

4. Provide a Name and optional Description for the lookup table

5. Drag a CSV file or click Browse to upload your reference data

6. Review the automatically detected column names

7. Click Save to store the lookup table

Managing Lookup Tables

From the Lookup Tables tab, you can:

• Edit table descriptions or upload a new CSV

• Delete tables that are no longer needed

Using Table Lookup Transformers

Basic Syntax

To use a Table Lookup Transformer in a common or action-synced attribute:

1. In Destination Attribute, choose the attribute on the target entity that will be updated

2. In Formatter, choose the source attribute to transform

3. In Pipeline Functions, specify the lookup table name, the column to match against, and the column containing values to return.

Configuring an action-level attribute transformer using lookup tables.

The full syntax for using lookup table transformers is:

{<value> | LOOKUP <table_name>, <column_name>, <return_column_name>}

Where:

• <value> is the source attribute to transform (e.g., {location})

• <table_name> is the name of the lookup table to use

• <column_name> is the column in the table to match against

• <return_column_name> is the column containing the value to return

Examples

Assuming a user has "location": "IL001" and a lookup table named locationTable structured as shown earlier:

Formatter	Result
{location} | LOOKUP locationTable, location_code, city	"Chicago"
{location} | LOOKUP locationTable, location_code, state	"Illinois"
{location} | LOOKUP locationTable, location_code, state_code	"IL"

Advanced Features

Pipeline Transformations

You can combine lookup transformations with other transformation functions in a pipeline:

{location | LOOKUP locationTable, location_code, state_code | LOWER}

This would look up the state_code corresponding to the location value and convert it to lowercase.

Default Values

If you need to handle cases where a lookup value is not found in the table, you can implement this by including a "wildcard" or default row in your lookup table:

location_code,state_code,state,city
MN001,MN,Minnesota,Minneapolis
CA001,CA,California,Los Angeles
*,UNKNOWN,Unknown,Unknown Location

This enables any unmatched values to return a default mapping instead of failing.

Technical Details

Implementation Notes

• Lookup tables are immutable and automatically deleted when no longer referenced by any policy version

• Multiple policy versions can reference the same lookup table (e.g., an active version and a draft version)

• Lookup tables are defined at the policy level and can be referenced by any transformer within the policy

• Lookup tables can have multiple columns to support different transformations from the same reference data

Best Practices

1. Standardize Naming: To use a lookup-based transformer, you will reference the table by file name. Apply consistent conventions for both the table and columns.

2. Document Mappings: Add descriptions for each lookup table to explain its purpose

3. Validate Data: Ensure lookup tables are complete and accurate before using them in transformers. Consider how lookup tables will be maintained over time, especially for values expected to change.

Troubleshooting

Common Issues

Issue	Resolution
Value not found in lookup table	Add the missing mapping to the lookup table or add a default/wildcard entry
Incorrect column name referenced	Check the column names in your lookup table (they are case-sensitive)
Unexpected transformation results	Verify the lookup table content and ensure the correct columns are specified

Related Topics

• Attribute Transformers
• Common Transformers
• Pipeline Functions
• Lifecycle Management Workflows