{
  "id": "google-cloud-data-fusion-vs-azure-data-factory",
  "ids": [
    "google-cloud-data-fusion",
    "azure-data-factory"
  ],
  "title": "Google Cloud Data Fusion vs Azure Data Factory: data pipelines, connectors and operating scope",
  "heading": "Google Cloud Data Fusion vs Azure Data Factory",
  "kicker": "Software architecture comparison",
  "deck": "Data Fusion provides CDAP-based visual integration in Google Cloud; Data Factory provides Azure pipelines, activities and integration runtimes. Test the required connectors and transformation behaviour using the same representative data.",
  "description": "Compare Google Cloud Data Fusion vs Azure Data Factory for data pipelines, connectors and operating scope.",
  "answerHeading": "Which option fits the requirement?",
  "answer": "Data Fusion provides CDAP-based visual integration in Google Cloud; Data Factory provides Azure pipelines, activities and integration runtimes. Test the required connectors and transformation behaviour using the same representative data.",
  "checkedAt": "2026-10-25",
  "researchTag": "Deployment, compatibility and cost scope",
  "researchLimit": "Official product material reviewed. Acceptance checks are proposed exercises, not completed product trials, performance measurements or reliability guarantees.",
  "datasetDescription": "Seven technical differences and four proposed acceptance checks for Google Cloud Data Fusion vs Azure Data Factory.",
  "type": "workflow-buying",
  "sharedBuyingNeed": "Evaluate alternative approaches to data pipelines, connectors and operating scope.",
  "rows": [
    [
      "Provider context",
      "Google Cloud",
      "Microsoft Azure"
    ],
    [
      "Integration scope",
      "Managed data integration based on CDAP",
      "Managed data integration and orchestration"
    ],
    [
      "Pipeline development",
      "Visual pipeline design and plugins",
      "Pipelines and activities"
    ],
    [
      "Execution context",
      "Selected Data Fusion instance and execution resources",
      "Selected integration runtime and activities"
    ],
    [
      "Connection configuration",
      "Source and destination connections",
      "Linked services and datasets"
    ],
    [
      "Validation focus",
      "Transformations, schema and pipeline recovery",
      "Transformations, schema and pipeline recovery"
    ],
    [
      "Cost components",
      "Instance scope, execution resources and network transfer",
      "Selected activities, runtime usage and data movement"
    ]
  ],
  "scenarios": [
    {
      "name": "Initial load",
      "question": "Can the team move a representative dataset through the proposed pipeline?",
      "left": "With Google Cloud Data Fusion, move a representative dataset through the proposed pipeline.",
      "right": "With Azure Data Factory, move a representative dataset through the proposed pipeline.",
      "note": "Required records and data types arrive correctly."
    },
    {
      "name": "Update cycle",
      "question": "Can the team process a controlled set of new and changed records?",
      "left": "With Google Cloud Data Fusion, process a controlled set of new and changed records.",
      "right": "With Azure Data Factory, process a controlled set of new and changed records.",
      "note": "The destination reflects the expected updates."
    },
    {
      "name": "Schema change",
      "question": "Can the team introduce a disposable source schema change?",
      "left": "With Google Cloud Data Fusion, introduce a disposable source schema change.",
      "right": "With Azure Data Factory, introduce a disposable source schema change.",
      "note": "The workflow handles or reports the change as specified."
    },
    {
      "name": "Failure recovery",
      "question": "Can the team interrupt and recover a disposable pipeline run?",
      "left": "With Google Cloud Data Fusion, interrupt and recover a disposable pipeline run.",
      "right": "With Azure Data Factory, interrupt and recover a disposable pipeline run.",
      "note": "The recovery process does not create unintended duplicate data."
    }
  ],
  "exampleTable": {
    "caption": "Proposed checks using authorised non-production resources. Record the actual account, region, version, configuration and observed outcome.",
    "headers": [
      "Requirement",
      "Proposed exercise",
      "Accept when"
    ],
    "rows": [
      [
        "Initial load",
        "Move a representative dataset through the proposed pipeline.",
        "Required records and data types arrive correctly."
      ],
      [
        "Update cycle",
        "Process a controlled set of new and changed records.",
        "The destination reflects the expected updates."
      ],
      [
        "Schema change",
        "Introduce a disposable source schema change.",
        "The workflow handles or reports the change as specified."
      ],
      [
        "Failure recovery",
        "Interrupt and recover a disposable pipeline run.",
        "The recovery process does not create unintended duplicate data."
      ]
    ]
  },
  "sections": [
    {
      "id": "scope-and-architecture",
      "title": "Scope and architecture",
      "paragraphs": [
        "Data Fusion provides CDAP-based visual integration in Google Cloud; Data Factory provides Azure pipelines, activities and integration runtimes. Test the required connectors and transformation behaviour using the same representative data.",
        "Select source systems, destinations, transformation logic and refresh requirements before comparing data integration platforms. A connector name alone does not establish support for every required field, object or change-capture pattern."
      ],
      "links": [
        {
          "url": "https://docs.cloud.google.com/data-fusion/docs?hl=en",
          "label": "Google Cloud Data Fusion product information"
        },
        {
          "url": "https://azure.microsoft.com/en-us/products/data-factory/",
          "label": "Azure Data Factory product information"
        }
      ],
      "navLabel": "Architecture"
    },
    {
      "id": "cost-and-migration",
      "title": "Cost and migration planning",
      "paragraphs": [
        "For Google Cloud Data Fusion, include instance scope, execution resources and network transfer. For Azure Data Factory, include selected activities, runtime usage and data movement. Use the same workload and resilience assumptions, then confirm current vendor terms for the selected configuration.",
        "Run a representative initial load and update cycle. Verify data types, schema changes, credentials and failure recovery, then inspect the infrastructure and execution resources used by the pipeline."
      ],
      "navLabel": "Costs & migration"
    }
  ],
  "faqs": [
    {
      "question": "How should we choose between Google Cloud Data Fusion and Azure Data Factory?",
      "answer": "Data Fusion provides CDAP-based visual integration in Google Cloud; Data Factory provides Azure pipelines, activities and integration runtimes. Test the required connectors and transformation behaviour using the same representative data."
    },
    {
      "question": "Which costs belong in the comparison?",
      "answer": "For Google Cloud Data Fusion, include instance scope, execution resources and network transfer. For Azure Data Factory, include selected activities, runtime usage and data movement. Use the same workload and resilience assumptions, then confirm current vendor terms for the selected configuration."
    },
    {
      "question": "What should we validate before a production move?",
      "answer": "Run a representative initial load and update cycle. Verify data types, schema changes, credentials and failure recovery, then inspect the infrastructure and execution resources used by the pipeline."
    }
  ],
  "sources": [
    {
      "url": "https://docs.cloud.google.com/data-fusion/docs?hl=en",
      "facts": "Official product scope, deployment and integration context used for data pipelines, connectors and operating scope."
    },
    {
      "url": "https://azure.microsoft.com/en-us/products/data-factory/",
      "facts": "Official product scope, deployment and integration context used for data pipelines, connectors and operating scope."
    }
  ],
  "relatedLinks": [
    {
      "path": "/categories/data-integration-engineering/",
      "title": "Data Integration Engineering software"
    },
    {
      "path": "/guides/cloud-cost-checklist/",
      "title": "Cloud workload and cost checklist"
    }
  ]
}