Data Changes

The Data Changes display how tables are impacted by data source reprofiling. This includes changes in:

• Row Count

• Popularity Score

• Importance Score

It also compares new profiling results to previous ones.

For example, if the TGT_employees table in SQL Server has a Popularity Score that increases from 80 to 100 after re-profiling, the updated score (100) will be displayed in the "Popularity Changes" column.

Unified View

The Unified View shows all changes to tables after re-profiling a data source. Users can filter, search, and sort by:

• Connector Name

• Schema

• Table Title

• Business Description

• Last Profiled Date

Changes displayed for each table include:

• Row Count: See how many rows have been added or removed.

• Popularity: Track how often users interact with the table (views, endorsements, comments, tags, queries).

• Importance: Understand a table's significance (0-100) based on relationships with other data.

Drilling into Table Changes

Selecting a table name in Data Changes takes users to a detailed comparison of the table's most recent profiling session with the previous one. This comparison highlights changes in the following:

• Profiled Date: See when the latest profiling occurred.

• Row Count: Track changes in the number of table rows.

• Popularity Score: Understand how user interactions (views, endorsements, etc.) have changed.

• Importance Score: Measure the table's evolving significance (0-100) based on the relationships between data.

Column-Level Changes:

The comparison also shows specific changes for each table column.

• Column position: See if a column's order within the table has been rearranged. Differences in the number of columns between the source and the target are highlighted.

• Maximum value: See if the highest value in a column has changed.

• Column position: See if a column's order within the table has been rearranged. Differences in the number of columns between the source and the target are highlighted.

• Column name: Monitor if a column's name has been modified.

• Column type: Understand if a column's data type has been altered (e.g., from int to varchar).

• Top values: Track variations in a column's 50 most frequent values.

• Null count: Identify differences in the number of null values in a column.

• Distinct count: Gauge fluctuations in the number of unique values within a column.

• Minimum value: See if the lowest value in a column has changed.