KEY TAKEAWAYAWS Glue and Azure Data Factory are both cloud-native data integration tools, but they solve different problems at different levels of the data pipeline.
Key comparison:
- Architecture: AWS Glue is a serverless, Spark-based ETL service, while Azure Data Factory is primarily an orchestration and data movement platform.
- Functionality: Glue handles complex, code-driven transformations, whereas ADF focuses on coordinating pipelines and relies on external services for heavy transforms.
- Integration: Glue integrates deeply within the AWS ecosystem, while ADF offers broader out-of-the-box connectors for SaaS, on-premises, and hybrid environments.
- Cost: Glue uses usage-based, compute-driven pricing, while ADF follows an activity-based model that is often easier to predict for scheduled workloads.
When to choose?
Choose AWS Glue if you need scalable, Spark-based transformations within an AWS-first stack. Choose Azure Data Factory if you prioritize low-code pipeline orchestration and hybrid data integration.
Choosing between AWS Glue and Azure Data Factory comes down to how much complexity your team is willing to manage to keep pipelines reliable.
Both are powerful, cloud-native services designed to help teams move and transform data. But in practice, teams often struggle with custom Spark code and dealing with costs that are hard to predict as data volumes grow.
This guide compares AWS Glue vs Azure Data Factory from a practical standpoint, assessing their architecture, ease of use, transformation capabilities, scalability, and pricing. You’ll understand which tool is ideal for your data stack.
Table of Contents
AWS Glue vs Azure Data Factory vs Hevo: Comparison Table
 Try Hevo for Free |  |  | |
| Type | No-code | Serverless | Hybrid |
| UI | Visual, intuitive | Basic visual + code editor | Drag-and-drop |
| Code-free Option | Yes | Partial | Yes |
| Native CDC Support | Out-of-box real-time CDC | Available (via configuration) | Supported through custom logic |
| Prebuilt Connectors | 150+ battle-tested | 60+ (AWS-centric) | 90+ (Cloud, SaaS, on-premises) |
| Scheduling & Triggers | Yes | Built-in | Advanced |
| Pricing Model | Transparent | Compute time (DPU-based) | Activity-based |
| Learning Curve | Low | Steep | Moderate |
| Ideal For | Ease of use and fast setup | AWS-centric ETL with custom logic | Hybrid/multicloud ETL orchestration |
What Are AWS Glue and Azure Data Factory?
AWS Glue
AWS Glue is a fully managed, serverless ETL service from Amazon Web Services that helps data teams build and run data integration pipelines at scale. Since it’s serverless, you pay only for the compute time used during job execution.
Glue integrates natively with AWS services like Amazon S3, Redshift, RDS, and Athena, making it well-suited for teams operating primarily within the AWS ecosystem. It supports both code-based ETL using PySpark or Scala and visual job creation, allowing teams to handle everything from simple data preparation to complex transformation logic.
AWS Glue also includes a central Data Catalog, automated crawlers for schema discovery, and built-in scheduling to streamline data discovery, transformation, and orchestration.
Key features:
- Serverless ETL with automatic scaling.
- Code-based transformations using PySpark and Scala.
- Built-in scheduling, triggers, and workflows.
- Centralized metadata management via Glue Data Catalog.
- Support for incremental processing and CDC using job bookmarks.
Pros:
- Deep, native integration with AWS data and analytics services.
- Flexible for complex, large-scale ETL workloads.
- Centralized schema and metadata management.
Cons:
- Steeper learning curve due to reliance on Spark and coding.
- Limited prebuilt connectors for SaaS applications.
- Usage-based pricing can be hard to predict at higher volumes.
Azure Data Factory
Azure Data Factory (ADF) is a cloud-based data integration and orchestration service from Microsoft that enables teams to build, schedule, and monitor data pipelines using a low-code, visual interface.
ADF focuses on pipeline orchestration and data movement, with native integration across Azure services (Synapse, Data Lake, SQL DB, Power BI) and support for cloud, on-premises, and hybrid environments.
One of ADF’s key strengths is its drag-and-drop pipeline builder, flexible triggers, and extensive connector library, which allows teams to integrate data sources quickly without writing extensive code.
Key features:
- Visual, low-code pipeline builder for rapid development.
- Native integration with Azure analytics and storage services.
- 90+ prebuilt connectors for SaaS, databases, and on-premises systems.
- Flexible scheduling with time-based, event-based, and tumbling window triggers.
- Hybrid data integration via Azure Integration Runtime.
Pros:
- Easy to use for both technical and non-technical users.
- Broad connector ecosystem for fast data integration.
- Strong pipeline scheduling, monitoring, and parameterization.
Cons:
- Advanced transformations and CDC require additional services (e.g., Synapse, Databricks).
- Scaling and performance tuning may require manual configuration.
- Limited support for real-time or streaming ETL workloads.
Unlike AWS Glue or Azure Data Factory, Hevo is designed for modern data teams that want reliable, real-time data movement without managing infrastructure, writing jobs, or debugging pipelines.
Why teams choose Hevo over Glue or ADF:
- No code: Hevo eliminates job orchestration, cluster management, and dependency handling.
- Built for real-time: Native support for streaming and near-real-time ingestion, no complex workarounds or add-ons.
- Cloud-agnostic by design: Move data across warehouses and SaaS tools without being locked into AWS or Azure.
- Faster time to value: Set up pipelines in minutes, not weeks of configuration and testing.
- Lower total cost of ownership: No surprise costs from compute tuning, retries, or pipeline failures.
The result: Cleaner data, faster analytics, and a data team focused on insights
AWS Glue vs Azure Data Factory: Feature-by-Feature Comparison
1. Deployment & ease of use
- AWS Glue: Serverless ETL with setup fully managed by AWS. Offers both code-based (PySpark/Scala) and visual ETL, but a learning curve exists for Spark and Python users.
- Azure Data Factory: Low-code, drag-and-drop interface makes pipeline creation quick and accessible for technical and non-technical users.
Result: Teams seeking low-code simplicity and quick deployment may prefer ADF, while engineering-heavy teams may favor AWS Glue for full control.
2. Connector coverage & integration flexibility
- AWS Glue: Strong native integration with AWS services; fewer prebuilt SaaS connectors, often requiring custom code. Integrations are typically implemented through AWS-native services or custom Spark and Python connectors.
- Azure Data Factory: Over 90 prebuilt connectors for databases, SaaS, and on-premises systems, offering broad integration flexibility. Connectors are available through managed linked services with minimal configuration.
Result: Organizations needing extensive out-of-the-box connectors and hybrid integration may find ADF more convenient.
3. Transformation & orchestration capabilities
- AWS Glue: Powerful for complex transformations with PySpark or Scala; supports both batch and streaming ETL. Transformations run on managed Apache Spark, allowing fine-grained control over data logic and performance.
- Azure Data Factory: Orchestration-focused, best for batch and near real-time pipelines; advanced transformations often require integration with Synapse, Databricks, or custom compute.
Result: For advanced code-driven transformations, AWS Glue is preferred; for pipeline orchestration with minimal coding, ADF is more suitable.
4. Scalability, reliability & maintenance
- AWS Glue: Serverless scaling handles large volumes automatically; requires knowledge of Spark for optimization. Job performance and resource allocation are managed by AWS, with tuning handled at the Spark configuration level.
- Azure Data Factory: Scaling via Integration Runtimes may require manual tuning; monitoring and pipeline management are intuitive. Execution capacity and reliability are controlled through Integration Runtime configuration and built-in monitoring tools.
Result: AWS Glue is better suited for highly scalable, compute-intensive workloads, while ADF aligns well with teams seeking simpler pipeline monitoring and operational control.
5. Pricing model & cost transparency
- AWS Glue: Pay-per-use pricing based on compute time consumed by ETL jobs; costs vary with job duration and resource usage. Pricing is tied to Data Processing Units (DPUs), which can fluctuate with data volume and transformation complexity.
- Azure Data Factory: Pay-per-activity, data movement, and pipeline execution; generally more predictable for scheduled batch workloads. Costs are calculated per pipeline activity and Integration Runtime usage, offering clearer cost attribution per workflow.
Result: ADF offers more predictable pricing for scheduled pipelines, while AWS Glue provides flexible, usage-based costs for variable or compute-intensive workloads.
Why Does Hevo Stand Out?
While AWS Glue and Azure Data Factory are powerful native services, they often require teams to manage infrastructure decisions, write custom code, or optimize across multiple services for advanced use cases such as CDC, SaaS integrations, and multi-cloud pipelines.
Hevo takes a different approach. It focuses on simplifying ELT for modern data teams by offering a fully managed, no-code platform with built-in connectors, native CDC, and predictable pricing. Instead of configuring Spark jobs, teams can move data reliably with minimal setup and operational overhead.
For organizations working across multiple clouds, SaaS tools, and warehouses, Hevo reduces complexity while maintaining performance and scalability. Teams without deep technical expertise can easily build secure pipelines to meet their requirements.
Sign up for Hevo’s 14-day free trial and see how Hevo simplifies multi-cloud ELT compared to native services.
FAQs on Azure Data Factory vs AWS Glue
Is AWS Glue free?
No, AWS Glue is not free. Charges apply based on the usage time per worker for each job and for data catalog use. Pricing can fluctuate based on job duration, resource allocation, and data volumes processed by your pipelines.
Can Azure Data Factory perform real-time ETL?
Azure Data Factory is primarily designed for batch processing of data. While it offers some capabilities for near-real-time ingestion, it does not natively provide full real-time ETL support. It works best for scheduled or event-driven pipelines where timely, but not instant, processing is sufficient.
Does either tool support Change Data Capture (CDC)?
AWS Glue can be configured to support CDC through database migration services and custom Spark setups. Azure Data Factory requires custom solutions for CDC use cases. In contrast, Hevo supports out-of-the-box real-time CDC without additional configuration or code.
Which tool is easier to learn: AWS Glue or Azure Data Factory?
Azure Data Factory is typically easier to learn due to its visual, no-code interface and prebuilt connectors. AWS Glue’s requirement for coding with Spark, Python, or Scala introduces a steeper learning curve, making it better suited to development-skilled teams.
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