OpenMetadata/openmetadata-docs/content/v1.7.x/how-to-guides/data-governance/classification/Auto Classification/external-workflow.md

title	slug
External Auto Classification Workflow	/how-to-guides/data-governance/classification/auto-classification/external-workflow

Auto Classification Workflow Configuration

The Auto Classification Workflow enables automatic tagging of sensitive information within databases. Below are the configuration parameters available in the Service Classification Pipeline JSON.

Pipeline Configuration Parameters

Parameter	Description	Type	Default Value
type	Specifies the pipeline type.	String	AutoClassification
classificationFilterPattern	Regex to compute metrics for tables matching specific tags, tiers, or glossary patterns.	Object	N/A
schemaFilterPattern	Regex to fetch schemas matching the specified pattern.	Object	N/A
tableFilterPattern	Regex to exclude tables matching the specified pattern.	Object	N/A
databaseFilterPattern	Regex to fetch databases matching the specified pattern.	Object	N/A
includeViews	Option to include or exclude views during metadata ingestion.	Boolean	true
useFqnForFiltering	Determines whether filtering is applied to the Fully Qualified Name (FQN) instead of raw names.	Boolean	false
storeSampleData	Option to enable or disable storing sample data for each table.	Boolean	true
enableAutoClassification	Enables automatic tagging of columns that might contain sensitive information.	Boolean	false
confidence	Confidence level for tagging columns as sensitive. Value ranges from 0 to 100.	Number	80
sampleDataCount	Number of sample rows to ingest when Store Sample Data is enabled.	Integer	50

Key Parameters Explained

enableAutoClassification

• Set this to true to enable automatic detection of sensitive columns (e.g., PII).
• Applies pattern recognition and tagging based on predefined criteria.

confidence

• Confidence level for tagging sensitive columns:
  • A higher confidence value (e.g., 90) reduces false positives but may miss some sensitive data.
  • A lower confidence value (e.g., 70) identifies more sensitive columns but may result in false positives.

storeSampleData

• Controls whether sample rows are stored during ingestion.
• If enabled, the specified number of rows (sampleDataCount) will be fetched for each table.

useFqnForFiltering

• When set to true, filtering patterns will be applied to the Fully Qualified Name of a table (e.g., service_name.db_name.schema_name.table_name).
• When set to false, filtering applies only to raw table names.

Auto Classification Workflow Execution

To execute the Auto Classification Workflow, follow the steps below:

1. Install the Required Python Package

Ensure you have the correct OpenMetadata ingestion package installed, including the PII Processor module:

pip install "openmetadata-ingestion[pii-processor]"

2. Define and Execute the Python Workflow

Instead of using a YAML configuration, use the AutoClassificationWorkflow from OpenMetadata to trigger the ingestion process programmatically.

Sample Auto Classification Workflow yaml

source:
  type: bigquery
  serviceName: local_bigquery
  serviceConnection:
    config:
      type: BigQuery
      credentials:
        gcpConfig:
          type: service_account
          projectId: my-project-id-1234
          privateKeyId: privateKeyID
          privateKey: "-----BEGIN PRIVATE KEY-----\nmySuperSecurePrivateKey==\n-----END PRIVATE KEY-----\n"
          clientEmail: client@email.secure
          clientId: "1234567890"
          authUri: https://accounts.google.com/o/oauth2/auth
          tokenUri: https://oauth2.googleapis.com/token
          authProviderX509CertUrl: https://www.googleapis.com/oauth2/v1/certs
          clientX509CertUrl: https://www.googleapis.com/oauth2/v1/certs
  sourceConfig:
    config:
      type: AutoClassification
      storeSampleData: true
      enableAutoClassification: true
      databaseFilterPattern:
        includes: 
          - hello-world-1234
      schemaFilterPattern:
        includes: 
          - super_schema
      tableFilterPattern:
        includes: 
          - abc

processor:
   type: "orm-profiler"
   config:
    tableConfig:
      - fullyQualifiedName: local_bigquery.hello-world-1234.super_schema.abc
        profileSample: 85
        partitionConfig:
          partitionQueryDuration: 180
        columnConfig:
          excludeColumns:
            - a
            - b

sink:
  type: metadata-rest
  config: {}
workflowConfig:
#  loggerLevel: INFO # DEBUG, INFO, WARN or ERROR
  openMetadataServerConfig:
    hostPort: http://localhost:8585/api
    authProvider: openmetadata
    securityConfig:
      jwtToken: "eyJraWQiOiJHYjM4OWEtOWY3Ni1nZGpzLWE5MmotMDI0MmJrOTQzNTYiLCJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJzdWIiOiJhZG1pbiIsImlzQm90IjpmYWxzZSwiaXNzIjoib3Blbi1tZXRhZGF0YS5vcmciLCJpYXQiOjE2NjM5Mzg0NjIsImVtYWlsIjoiYWRtaW5Ab3Blbm1ldGFkYXRhLm9yZyJ9.tS8um_5DKu7HgzGBzS1VTA5uUjKWOCU0B_j08WXBiEC0mr0zNREkqVfwFDD-d24HlNEbrqioLsBuFRiwIWKc1m_ZlVQbG7P36RUxhuv2vbSp80FKyNM-Tj93FDzq91jsyNmsQhyNv_fNr3TXfzzSPjHt8Go0FMMP66weoKMgW2PbXlhVKwEuXUHyakLLzewm9UMeQaEiRzhiTMU3UkLXcKbYEJJvfNFcLwSl9W8JCO_l0Yj3ud-qt_nQYEZwqW6u5nfdQllN133iikV4fM5QZsMCnm8Rq1mvLR0y9bmJiD7fwM1tmJ791TUWqmKaTnP49U493VanKpUAfzIiOiIbhg"

3. Expected Outcome

• Automatically classifies and tags sensitive data based on predefined patterns and confidence levels.
• Improves metadata enrichment and enhances data governance practices.
• Provides visibility into sensitive data across databases. This approach ensures that the Auto Classification Workflow is executed correctly using the appropriate OpenMetadata ingestion framework.

{% partial file="/v1.7/connectors/yaml/auto-classification.md" variables={connector: "snowflake"} /%}

Workflow Execution

To Execute the Auto Classification Workflow:

1. Create a Pipeline

   • Configure the Auto Classification JSON as demonstrated in the provided configuration example.

2. Run the Ingestion Pipeline

   • Use OpenMetadata or an external scheduler like Argo to trigger the pipeline execution.

3. Validate Results

   • Verify the metadata and tags applied to sensitive columns in the OpenMetadata UI.

Expected Outcomes

• Automatic Tagging:
  Columns containing sensitive information (e.g., names, emails, SSNs) are automatically tagged based on predefined confidence levels.

• Enhanced Visibility: Gain improved visibility and classification of sensitive data within your databases.

• Sample Data Integration:
  Store sample data to provide better insights during profiling and testing workflows.