iiRecord
Agentic AI Atlas · data-quality-framework
lib-process:data-engineering-analytics--data-quality-frameworka5c.ai
II.
LibraryProcess JSON

lib-process:data-engineering-analytics--data-quality-framework

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data-quality-framework json

Inspect the normalized record payload exactly as the atlas UI reads it.

File · generated-library/processes.yamlCluster · generated-library
Record JSON
{
  "id": "lib-process:data-engineering-analytics--data-quality-framework",
  "_kind": "LibraryProcess",
  "_file": "generated-library/processes.yaml",
  "_cluster": "generated-library",
  "attributes": {
    "displayName": "data-quality-framework",
    "description": "Implement comprehensive data quality framework with dimensions, validation rules, monitoring, alerting, anomaly detection, and data profiling",
    "libraryPath": "library/specializations/data-engineering-analytics/data-quality-framework.js",
    "specialization": "data-engineering-analytics",
    "references": [
      "- Great Expectations: https://greatexpectations.io/\n- dbt Tests: https://docs.getdbt.com/docs/build/tests\n- Data Quality Dimensions: https://www.dataversity.net/six-key-dimensions-data-quality/\n- Anomaly Detection: https://scikit-learn.org/stable/modules/outlier_detection.html\n- Data Profiling: https://github.com/ydataai/ydata-profiling"
    ],
    "example": "const result = await orchestrate('data-engineering-analytics/data-quality-framework', {\n  dataSources: [\n    { type: 'database', connection: 'postgres://...', tables: ['users', 'orders'] },\n    { type: 'file', path: 'data/transactions.parquet' }\n  ],\n  qualityDimensions: ['accuracy', 'completeness', 'consistency', 'validity', 'timeliness', 'uniqueness'],\n  validationRules: {\n    accuracy: ['no_outliers_beyond_3_std', 'reference_data_match'],\n    completeness: ['required_fields_present', 'no_null_in_critical_columns'],\n    consistency: ['cross_table_integrity', 'format_consistency'],\n    validity: ['email_format', 'positive_amounts', 'valid_date_ranges'],\n    timeliness: ['data_age_within_24h', 'no_future_dates'],\n    uniqueness: ['primary_key_unique', 'no_duplicate_records']\n  },\n  thresholds: {\n    critical: 95,\n    warning: 85,\n    minimum: 70\n  },\n  monitoringEnabled: true,\n  alertingConfig: {\n    channels: ['email', 'slack'],\n    recipients: ['data-team@company.com'],\n    severityLevels: ['critical', 'high']\n  },\n  profilingEnabled: true\n});",
    "usesAgents": [
      "general-purpose"
    ]
  },
  "outgoingEdges": [
    {
      "from": "lib-process:data-engineering-analytics--data-quality-framework",
      "to": "domain:data-engineering",
      "kind": "lib_applies_to_domain",
      "attributes": {
        "weight": 1
      }
    },
    {
      "from": "lib-process:data-engineering-analytics--data-quality-framework",
      "to": "workflow:code-review",
      "kind": "lib_implements_workflow",
      "attributes": {
        "weight": 1
      }
    },
    {
      "from": "lib-process:data-engineering-analytics--data-quality-framework",
      "to": "specialization:data-engineering-analytics",
      "kind": "lib_belongs_to_specialization",
      "attributes": {
        "weight": 0.9
      }
    }
  ],
  "incomingEdges": []
}