Google Cloud Data Fusion vs Azure Data Factory
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.
Which option fits the requirement?
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.
Seven differences that change the shortlist
| Buying question | Google Cloud Data Fusion | Azure Data Factory |
|---|---|---|
| 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 |
Four worked buying briefs
These are hypothetical scenarios with explicit assumptions. They do not establish equal capabilities, measured usability or a negotiated price.
| Requirement | Proposed exercise | Accept when |
|---|---|---|
| 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. |
Initial load
Can the team move a representative dataset through the proposed pipeline?
Google Cloud Data Fusion: With Google Cloud Data Fusion, move a representative dataset through the proposed pipeline.
Azure Data Factory: With Azure Data Factory, move a representative dataset through the proposed pipeline.
Required records and data types arrive correctly.
Update cycle
Can the team process a controlled set of new and changed records?
Google Cloud Data Fusion: With Google Cloud Data Fusion, process a controlled set of new and changed records.
Azure Data Factory: With Azure Data Factory, process a controlled set of new and changed records.
The destination reflects the expected updates.
Schema change
Can the team introduce a disposable source schema change?
Google Cloud Data Fusion: With Google Cloud Data Fusion, introduce a disposable source schema change.
Azure Data Factory: With Azure Data Factory, introduce a disposable source schema change.
The workflow handles or reports the change as specified.
Failure recovery
Can the team interrupt and recover a disposable pipeline run?
Google Cloud Data Fusion: With Google Cloud Data Fusion, interrupt and recover a disposable pipeline run.
Azure Data Factory: With Azure Data Factory, interrupt and recover a disposable pipeline run.
The recovery process does not create unintended duplicate data.
Scope and architecture
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.
Google Cloud Data Fusion product information ↗ · Azure Data Factory product information ↗
Cost and migration planning
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.
Questions before you choose
How should we choose between Google Cloud Data Fusion and Azure Data Factory?
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.
Which costs belong in the comparison?
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.
What should we validate before a production move?
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.
Turn the comparison into your own evaluation
Use the same criteria and priorities for both products, keep unknowns unassessed and record the plan and evidence behind your ratings.
Build a Google Cloud Data Fusion vs Azure Data Factory scorecard →Keep the assumptions with the figures
Use the date, billing cycle, units and limits with each example. Check current account terms before purchasing. Unverified prices and model costs stay unpriced.
Download the buying examples as JSON ↓Share the comparison
Use these examples when discussing the shortlist with your team.