{
  "id": "workflow:data-pipeline-monitoring",
  "_kind": "Workflow",
  "_file": "workflows/workflows/workflows-data.yaml",
  "_cluster": "workflows",
  "attributes": {
    "displayName": "Data Pipeline Monitoring",
    "workflowKind": "data",
    "triggerType": "event-driven",
    "typicalCadence": "continuous",
    "complexity": "single-team",
    "description": "Monitors data pipeline health, detects schema drift or data-quality\nanomalies, and alerts owners for investigation and repair.\n"
  },
  "outgoingEdges": [
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "role:data-scientist",
      "kind": "involves_role",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "role:ml-engineer",
      "kind": "involves_role",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "skill-area:python-data-pipelines",
      "kind": "requires_skill_area",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "skill-area:kafka-stream-processing",
      "kind": "requires_skill_area",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "skill-area:observability-pipeline",
      "kind": "requires_skill_area",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "domain:data-science",
      "kind": "applies_to_domain",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "domain:ml-ops",
      "kind": "applies_to_domain",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "responsibility:respond-incidents",
      "kind": "triggers_responsibility",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "org-unit:data-team",
      "kind": "performed_by_org_unit",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "org-unit:ml-team",
      "kind": "performed_by_org_unit",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "org-unit:ml-platform-team",
      "kind": "performed_by_org_unit",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "org-unit:data-platform-team",
      "kind": "performed_by_org_unit",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "role:data-scientist",
      "kind": "involves_role",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "role:ml-engineer",
      "kind": "involves_role",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "skill-area:python-data-pipelines",
      "kind": "requires_skill_area",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "skill-area:kafka-stream-processing",
      "kind": "requires_skill_area",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "skill-area:observability-pipeline",
      "kind": "requires_skill_area",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "domain:data-science",
      "kind": "applies_to_domain",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "domain:ml-ops",
      "kind": "applies_to_domain",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "responsibility:respond-incidents",
      "kind": "triggers_responsibility",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "org-unit:data-team",
      "kind": "performed_by_org_unit",
      "attributes": {}
    },
    {
      "from": "workflow:data-pipeline-monitoring",
      "to": "org-unit:ml-team",
      "kind": "performed_by_org_unit",
      "attributes": {}
    }
  ],
  "incomingEdges": [
    {
      "from": "stack-profile:data-pipeline-orchestration",
      "to": "workflow:data-pipeline-monitoring",
      "kind": "follows_workflow"
    },
    {
      "from": "tool:databricks",
      "to": "workflow:data-pipeline-monitoring",
      "kind": "supports_work",
      "attributes": {
        "confidence": "high",
        "evidence": "Jobs, notebooks, and warehouse metadata support pipeline monitoring."
      }
    },
    {
      "from": "tool:snowflake",
      "to": "workflow:data-pipeline-monitoring",
      "kind": "supports_work",
      "attributes": {
        "confidence": "high",
        "evidence": "Warehouse freshness and query metadata support data pipeline monitoring."
      }
    },
    {
      "from": "tool:dbt-labs",
      "to": "workflow:data-pipeline-monitoring",
      "kind": "supports_work",
      "attributes": {
        "confidence": "high",
        "evidence": "Ora surfaced dbt Labs for data engineering orchestration and warehouse workflows."
      }
    },
    {
      "from": "tool-server:mcp-fivetran",
      "to": "workflow:data-pipeline-monitoring",
      "kind": "supports_work",
      "attributes": {
        "confidence": "high",
        "evidence": "Fivetran connector metadata is a direct data-pipeline monitoring input."
      }
    },
    {
      "from": "tool-server:mcp-snowflake",
      "to": "workflow:data-pipeline-monitoring",
      "kind": "supports_work",
      "attributes": {
        "confidence": "high",
        "evidence": "Warehouse state and query results project pipeline health and data freshness."
      }
    },
    {
      "from": "tool-server:mcp-segment",
      "to": "workflow:data-pipeline-monitoring",
      "kind": "supports_work",
      "attributes": {
        "confidence": "medium",
        "evidence": "Event stream health and schema metadata feed data pipeline review."
      }
    },
    {
      "from": "tool-server:mcp-dbt-candidate",
      "to": "workflow:data-pipeline-monitoring",
      "kind": "supports_work",
      "attributes": {
        "confidence": "medium",
        "evidence": "dbt tests, lineage, and run results are pipeline-monitoring projection points."
      }
    },
    {
      "from": "tool-server:mcp-tinybird-candidate",
      "to": "workflow:data-pipeline-monitoring",
      "kind": "supports_work",
      "attributes": {
        "confidence": "medium",
        "evidence": "Tinybird is relevant to live data-product monitoring and analytics pipelines."
      }
    },
    {
      "from": "tool-server:mcp-power-bi-candidate",
      "to": "workflow:data-pipeline-monitoring",
      "kind": "supports_work",
      "attributes": {
        "confidence": "medium",
        "evidence": "Dashboard health and dataset refreshes are common analytics-monitoring projection points."
      }
    },
    {
      "from": "tool-server:mcp-databricks-candidate",
      "to": "workflow:data-pipeline-monitoring",
      "kind": "supports_work",
      "attributes": {
        "confidence": "medium",
        "evidence": "Databricks jobs and SQL warehouses are common data-pipeline monitoring surfaces."
      }
    }
  ]
}