AWS Glue vs Azure Data Factory
Compare AWS Glue and Azure Data Factory for data movement, transformations, metadata, hybrid connectivity and the separate resources required to run a pipeline.
Which pipeline approach fits?
Start with AWS Glue for an AWS-centred integration and processing architecture, and Azure Data Factory for Azure-centred pipeline coordination and movement. Then compare the actual source, execution engine and destination. Glue jobs and Data Factory activities are not one interchangeable billing unit. Microsoft also offers Data Factory in Fabric, which is a separate product arrangement to scope before a new implementation.
Seven differences that change the shortlist
| Buying question | AWS Glue | Azure Data Factory |
|---|---|---|
| Native context | AWS data integration, job execution and Data Catalog | Azure pipeline orchestration, movement and transformation activities |
| Execution scope | Specify the Glue job engine, workers and feature | Specify copy, data flow, control or external compute activity |
| Metadata | Glue Data Catalog and related catalog operations | Datasets and linked services; wider catalog needs separate scope |
| Hybrid connectivity | Define the source network, connector and execution path | Define Azure or self-hosted integration runtime and access |
| Transformation | Select the supported job and development arrangement | Use mapping data flows or the chosen external compute service |
| Cost inventory | Job resources, catalog, quality and connected-service usage | Orchestration, movement, data-flow and external-service usage |
| Product boundary | Glue does not include every AWS storage or analytics charge | Azure Data Factory is distinct from Data Factory in Fabric |
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 |
|---|---|---|
| Connectivity | Trace the runtime-to-source network path | Access and credential owners are documented |
| Correct output | Reconcile a representative transformed table | The output satisfies the agreed data contract |
| Recovery | Rerun a single failed date in isolation | No unintended duplicate or missing output remains |
| Cost inventory | List runtime and connected-service usage | The estimate includes more than the scheduler charge |
On-premises extract
Can the selected runtime reach an approved internal database?
AWS Glue: Document the network route, credentials and connector needed by the job.
Azure Data Factory: Document the integration runtime and linked-service route.
Use a test source and minimum required access; cloud hosting alone does not establish connectivity.
Daily transform
Will a scheduled job produce a reconciled reporting table?
AWS Glue: Run the chosen Glue engine with the agreed input and output contract.
Azure Data Factory: Run the chosen activity and transformation engine against the same contract.
Record transformation compute separately from the orchestration step that starts it.
Failed partition
Can an engineer rerun one failed date without duplicating output?
AWS Glue: Define job parameters, checkpoint or output-commit behaviour for the date.
Azure Data Factory: Define pipeline parameters, activity retries and destination write behaviour.
A green retry is insufficient unless the resulting rows are reconciled.
Full monthly scope
Does the estimate include development, retries and connected services?
AWS Glue: Inventory job resources, catalog operations, quality tasks and storage.
Azure Data Factory: Inventory activities, runtimes, data flows and external compute.
Include a credible busy-day and failure-recovery workload; do not assume every evaluation credit recurs.
Follow the activity to its compute
Draw the source, collection route, transformation runtime and destination. Data Factory can start work in another compute service, and that service has its own usage and operational requirements. Glue also has different job and feature choices rather than one universal execution model.
For an internal source, name the runtime and network path that can actually reach it. Keep credential rotation, firewall changes and upgrades with a responsible owner. A successful cloud demonstration against public sample data does not validate a restricted company database connection.
Price the selected product boundary
Glue pricing separates processing from catalog and other capabilities. A Data Catalog allowance does not make processing free. Azure Data Factory similarly separates orchestration, movement, transformation and external activities. Include storage and downstream analytics on both sides.
Microsoft currently points new integration users towards Data Factory in Fabric while documenting Azure Data Factory separately. Record the selected service and migration requirement explicitly. Do not copy an Azure Data Factory estimate into a Fabric proposal or imply a retirement date that the documentation does not establish.
Questions before you choose
Are AWS Glue and Azure Data Factory complete replacements for each other?
They overlap in integration work but have different runtime, catalog and activity boundaries. Compare the same source-to-destination architecture.
Does Glue’s free catalog allowance include free ETL compute?
No. Catalog allowances and processing charges are distinct. Check the exact job and connected services.
Is Data Factory in Fabric the same pricing arrangement as Azure Data Factory?
No. Scope the specific service and its capacity or usage model separately.
Your practical evaluation pack
Download the pipeline checklist (CSV)
Four proposed checks with blank evidence and owner columns. Record the actual deployment, package, data volume and recovery requirements in your evaluation.
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 AWS Glue 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.