Virtual Tables
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The Virtual Tables feature in DataFakery Studio allows you to create and manage tables for mapping or auxiliary values, facilitating a variety of use cases such as mapping a large list of IDs to new values and ensuring that anonymized values correlate with IDs and labels. Here’s how to utilize the virtual tables effectively.
Creating and Managing Virtual Tables
Add Table: Click this button to create a new virtual table.
Edit Table: Select this option to modify the existing virtual table.
Delete Table: Use this button to remove an unwanted virtual table.
Table Export Options: Configure options for exporting your virtual tables.
Importing Data into Virtual Tables
Import Tables
Use this option to import data into your virtual tables.
Import Excel (.xlsx): Import content from an Excel file into the virtual table.
Import CSV (.csv): Import content from a CSV file into the virtual table.
Note: Ensure the first row of your file includes the column titles for proper mapping.
Steps to Create and Import Virtual Tables
Create Virtual Tables
Click on Add Table in the Virtual Tables section of the ribbon toolbar.
Enter a name for your new virtual table in the Virtual Table Editor.
Import Data:
Click on Import Tables in the Data Model Browser.
Choose to import from Excel or CSV.
Select your file and ensure the first row contains the column titles for proper alignment.
Edit and Manage Data:
After importing, you can edit the data directly in the Virtual Table Editor.
Save your changes by clicking Save and Close.
Use virtual tables to map large lists of IDs to new values.
Ensure anonymized values correlate accurately with IDs and labels, which is particularly useful for maintaining data integrity and relationships.
In this example, we will load the city name based on the phone number area code using a virtual table named CityMappingTable. This process involves creating the virtual table, importing the mapping data, and then utilizing the table to perform the mapping.
By utilizing virtual tables, you can efficiently manage auxiliary data, perform complex mappings, and ensure that your data anonymization processes maintain the necessary relationships and consistency.
