{
  "id": "google-cloud-data-fusion-vs-aws-glue",
  "ids": [
    "google-cloud-data-fusion",
    "aws-glue"
  ],
  "title": "Google Cloud Data Fusion vs AWS Glue: data pipelines, connectors and operating scope",
  "heading": "Google Cloud Data Fusion vs AWS Glue",
  "kicker": "Software architecture comparison",
  "deck": "Data Fusion and Glue can both support cloud data integration, but their development and execution environments differ. Compare the actual pipeline logic, source coverage and operating resources rather than assuming a visual flow and a Glue job are equivalent packages.",
  "description": "Compare Google Cloud Data Fusion vs AWS Glue for data pipelines, connectors and operating scope.",
  "answerHeading": "Which option fits the requirement?",
  "answer": "Data Fusion and Glue can both support cloud data integration, but their development and execution environments differ. Compare the actual pipeline logic, source coverage and operating resources rather than assuming a visual flow and a Glue job are equivalent packages.",
  "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 AWS Glue.",
  "type": "workflow-buying",
  "sharedBuyingNeed": "Evaluate alternative approaches to data pipelines, connectors and operating scope.",
  "rows": [
    [
      "Provider context",
      "Google Cloud",
      "AWS"
    ],
    [
      "Integration scope",
      "Managed data integration based on CDAP",
      "Managed data integration"
    ],
    [
      "Pipeline development",
      "Visual pipeline design and plugins",
      "Glue jobs and configured integration workflows"
    ],
    [
      "Execution context",
      "Selected Data Fusion instance and execution resources",
      "Selected job execution configuration"
    ],
    [
      "Connection configuration",
      "Source and destination connections",
      "Connections and catalogue integration"
    ],
    [
      "Validation focus",
      "Transformations, schema and pipeline recovery",
      "Transformations, schema and job recovery"
    ],
    [
      "Cost components",
      "Instance scope, execution resources and network transfer",
      "Job execution, configured catalogue usage and supporting resources"
    ]
  ],
  "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 AWS Glue, 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 AWS Glue, 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 AWS Glue, 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 AWS Glue, 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 and Glue can both support cloud data integration, but their development and execution environments differ. Compare the actual pipeline logic, source coverage and operating resources rather than assuming a visual flow and a Glue job are equivalent packages.",
        "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://aws.amazon.com/glue/",
          "label": "AWS Glue 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 AWS Glue, include job execution, configured catalogue usage and supporting resources. 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 AWS Glue?",
      "answer": "Data Fusion and Glue can both support cloud data integration, but their development and execution environments differ. Compare the actual pipeline logic, source coverage and operating resources rather than assuming a visual flow and a Glue job are equivalent packages."
    },
    {
      "question": "Which costs belong in the comparison?",
      "answer": "For Google Cloud Data Fusion, include instance scope, execution resources and network transfer. For AWS Glue, include job execution, configured catalogue usage and supporting resources. 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://aws.amazon.com/glue/",
      "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"
    }
  ]
}