Source Data Analysis

This article describes plugins that analyze source systems and calculate various metrics. The analysis is done based on data snapshots.

The plugins are:

• Empty Datasets Discard — excludes empty tables based on the data snapshot results. This plugin is available starting from Fabric V8.3.1.
• Data Quality Metrics — calculates various data quality metrics as described below. These metrics can then be applied for data masking and synthetic data generation.
• Option Set Analyzer — identifies fields that contain a limited number of distinct values (within a data sample) and saves them in an MTable. These metrics can then be applied for data masking and synthetic data generation. This plugin is available starting from Fabric V8.3.

All of the above plugins are inactive by default and must be activated through the Discovery Pipeline if needed.

Empty Datasets Discard

Some data sources may contain a large number of empty tables that are irrelevant for the Catalog and for further LU creation.

This plugin improves both Catalog usability and the LU development process. When activated, the plugin automatically discards all empty tables during the Discovery job, writing a message in the Fabric log (one message per schema):

"<num> empty datasets were removed from schema <schema name>"

The Catalog schema is then created without the discarded tables.

Data Quality Metrics

This plugin scans the data sample to calculate various data quality metrics. These metrics can then be used for masking and synthetic data generation.

• Data Sample Size — the actual number of values per column in the data sample.
  • The data sample is retrieved per the Catalog settings. For example, the default sample is 10% of the table size, with a minimum of 100 and a maximum of 500. However, the actual size of the data sample can vary depending on the table size.
• Distinct Values — the count of distinct values per column in the data sample.
  • This parameter helps to assess the variety or uniqueness of data within a column.
  • In addition, it can help to validate whether the data values are within an acceptable or predefined range. For example, if a column is expected to store binary values (Yes/No or true/false), the presence of additional distinct values may indicate data quality issues.
  • A high number of distinct values in a column, where fewer unique entries are expected, may indicate potential data anomalies, typographical errors, or other issues.
  • This calculation is performed for strings, integers and real numbers.
• Minimum Value, Maximum Value, Average, and Standard Deviation are basic statistical calculations performed on numeric or date columns in the data sample.
  • Establishing the existing range of values in the data can assist in verifying whether these values fall within expected or acceptable limits. This can help reveal potential errors, such as outliers or incorrect data entries (e.g., a negative age value).
  • Understanding the range of values helps ensure consistency across similar datasets. It can also support business decision-making by providing insights into variability and distribution.
  • As part of basic descriptive statistics, these metrics offer an initial insight into data distribution and can serve as a precursor to more advanced statistical analyses.
• Null Percentage — the percentage of null values per column.
  • This percentage is calculated for each column in non-empty tables. The Null Percentage property is added to the field's properties when the calculated value exceeds the plugin's threshold.
  • For example, when 30% of the values in a given field are null, the Null Percentage property will be added to the field with the value = 0.3. However, if 20% or fewer of the values in this field are null, then this property will not be added.

Option Set Analyzer

The purpose of this plugin is to identify fields with a limited number of distinct values within a data sample. It saves these values into a dedicated MTable, enabling their use in data masking and synthetic data generation.

Starting with Fabric V8.4, when running discovery on JSON Schema files (using the File Cataloging solution), the plugin identifies fields with an enum property and extracts those values directly from the schema rather than a data sample.

To identify fields with limited distinct values, consider these guidelines:

1. Ensure the field is non-PII to comply with privacy regulations and avoid the exposure of sensitive data.
2. The number of distinct values should be below either a plugin’s threshold (e.g., 0.05) or the Absolute Threshold input parameter, which defaults to 15.

Additional rules apply based on the plugin input parameters, as explained further in this article.

Once a field is identified as an Option Set, the property optionSet = true is created for it. Starting from Fabric V8.3.1, the classification = OPTION_SET is assigned to this field, unless it has already been classified. In the Catalog Settings, the OPTION_SET classification is mapped to the RandomOptionSet Actor for masking and synthetic data generation. The actor randomly selects a value from the catalog_field_option_set MTable, based on the input data platform, schema, dataset, class and field.

A separate MTable is generated for each data platform and schema to store the distinct values (and their distribution) identified by the plugin in a field. The MTable name format is:

catalog_field_option_set___<dataPlatform>_<schema>.csv (containing three underscores before the data platform name).

The image below shows an example of such an MTable:

The plugin's input parameters are described below:

• propertyName is the field property that the plugin creates when the field qualifies as an Option Set. By default, the property is named optionSet.

• absoluteThreshold defines the maximum number of distinct values allowed. If the proportion of distinct values in a sample exceeds the plugin’s threshold (0.05), the plugin checks the count against this absolute value (15). For example:

  • In a sample of 100 records, a field has 10 distinct values (0.1 proportion). While 0.1 exceeds the 0.05 threshold, the field still qualifies as an Option Set because 10 is less than the Absolute Threshold (15).

• fieldTypeIncludeList controls which field data types should be analyzed.

  • By default, fieldTypeIncludeList is set to STRING and INTEGER.
  • Supported values are: STRING, INTEGER, REAL, DATETIME, DATE and BOOLEAN.

• fieldNameIncludeList is an override list of field names to be included in the plugin's validation algorithm, even if they are identified as PII or belong to a small table (see the minSampleSize property).

• fieldNameExcludeList is an override list of field names to be excluded from the plugin's validation algorithm.

• incrementalMode (introduced in Fabric V8.3.1) defines how the plugin handles fields analyzed in previous Discovery Job executions. It has the following modes:

  • "Keep All" (default) — the plugin will not analyze the fields that have already been analyzed in a previous Discovery Job execution (even if the field does not have the 'Option Set' property). The plugin will only analyze new fields.
  • "Keep Existing" — the plugin will not analyze the fields that have already been analyzed in a previous Discovery Job execution and an 'Option Set' property was created for it. The plugin will analyze new fields and the existing fields that do not have this property.
  • "Evaluate All" — the plugin will analyze all fields.

• maxStringLength sets a limit on STRING size to prevent handling text files or complex structures within a field. The default value is 512 bytes.

• minSampleSize defines the minimum sample size for performing the plugin's algorithm to determine whether a field qualifies as an Option Set. Datasets with a smaller sample size are skipped. The default value is 100.