AWS Glue vs Azure Data Factory: ETL Comparison for Data Teams

Data teams often compare AWS Glue and Azure Data Factory because both solutions stand out as leading ETL tools within their respective cloud ecosystems. AWS Glue attracts teams embedded in AWS, valuing its serverless scale and customizable transformation capabilities. 

Azure Data Factory appeals to organizations favoring low-code, visual pipeline building and seamless integration with Microsoft’s environment. 

This guide explains each tool’s features, use cases, and trade-offs, showing you exactly when to consider Glue, when Data Factory makes sense, and when a no-code alternative like Hevo could be the better choice.

What is AWS Glue?

AWS Glue is a fully managed ETL (Extract, Transform, Load) service provided by Amazon Web Services. Glue is serverless, so you only pay for the time your ETL operations are running.

AWS Glue has seamless integration with services like S3, Redshift, RDS, and Athena. It’s also highly flexible, supporting both code-based and visual ETL creation. Hence, it is ideal for engineering teams working in the AWS ecosystem who want control over transformations and need to manage complex ETL logic.

AWS Glue also provides a data catalog, job schedulers, and crawlers to automate the data discovery, preparation, and transformation processes. It’s optimized to process structured and semi-structured data with the help of scalable ETL pipelines that can be built using Python or Spark.

Key features of AWS Glue

• Serverless scaling: Automatically allocates computing power for ETL jobs, minimizing costs and performance controls.
• Custom scripting: Native support for PySpark and Scala for tailored data transformation logic.
• Workflow automation: Built-in job scheduler and triggers, with integration options for advanced scheduling patterns.
• Centralized metadata management: Glue Data Catalog enables schema discovery, manual definitions, and cross-service compatibility.
• Change Data Capture support: Can implement CDC using database logs, time-based windows, or custom scripts with job bookmarks.

Pros of AWS Glue

• Native, seamless integration with AWS services for data storage, analytics, and processing.
• Flexible support for both code-driven and serverless ETL, handling batch and streaming pipelines.
• Central Data Catalog with automated schema discovery, supporting complex data lineage and versioning.

Cons of AWS Glue

• Steep learning curve; requires Python, Scala, or Spark expertise for effective use.
• Limited prebuilt connectors, especially for SaaS sources, often necessitate custom integration work.
• Pricing based on compute time can lead to unpredictable costs during high data volumes.

What is Azure Data Factory?

Azure Data Factory (ADF) is a cloud-based ETL and data orchestration platform from Microsoft. It offers a low-code alternative to creating and managing data pipelines through an intuitive UI. Since this is a Microsoft product, it has strong support and integration with Azure’s internal services and external data sources.

It helps engineering and IT teams to quickly transform and ship data across multicloud/hybrid environments. If your organization is already invested in Azure services, Azure Data Factory might be the right choice for transformation operations.

Azure Data Factory is very famous for its intuitive drag-and-drop UI, flexible pipeline triggers, and easy scheduling options. It provides over 90 prebuilt connectors, which makes integration with cloud and on-premises easy.

Key features of Azure Data Factory

• Visual pipeline builder: Drag-and-drop UI for assembling, configuring, and deploying data pipelines with minimal coding.
• Deep Azure integration: Effortless connections with services like Synapse Analytics, Data Lake, SQL Database, and Power BI.
• 90+ prebuilt connectors: Robust connector library for databases, SaaS, on-premises, and hybrid data environments.
• Flexible triggers: Schedule, tumbling window, and event-based triggers for precise control over pipeline execution.
• Hybrid data integration: Securely moves and transforms data between on-premises systems and cloud environments.
• ETL for Azure Synapse or SQL DB: ADF is used to extract, transform, and load (ETL) data into Azure Synapse Analytics or Azure SQL Database.

Pros of Azure Data Factory

• User-friendly visual workflow ideal for technical and non-technical team members.
• Broad out-of-the-box connector ecosystem for fast integration with diverse sources.
• Robust support for scheduling, monitoring, and parameterizing pipelines.
  

Cons of Azure Data Factory

• Additional infrastructure or setup is needed for advanced transformations and CDC scenarios.
• Scalability adjustments may require manual tuning via Integration Runtimes.
• Real-time ETL capabilities are limited; better suited for batch and near real-time loads.

AWS Glue vs Azure Data Factory vs Hevo: Comparison Table

Hevo provides simplicity and reliability with no-code operation, many connectors, clear pricing, and automated sync.

AWS Glue stands out for deep AWS integration and serverless scaling, while Azure Data Factory excels in visual design and broad Azure coverage.

AWS Glue vs Azure Data Factory: Feature-by-Feature Comparison

Code vs No-Code Approach

AWS Glue is optimized for code-driven ETL, supporting custom transformations using Python and Scala, though basic visual options exist.

Azure Data Factory is tailored for no-code or low-code ETL, empowering users to create pipelines with a drag-and-drop UI and prebuilt transforms.

Choose Glue if you require granular transformation control and coding flexibility, or select ADF for rapid, no-code pipeline assembly.

Connector Ecosystem

Glue covers AWS-native and popular data sources with around 60 connectors, but integrating with many SaaS apps usually requires custom solutions.

Azure Data Factory provides over 90 prebuilt connectors, enabling fast connections to a range of cloud, SaaS, and hybrid environments without custom code.

ADF fits scenarios demanding diverse and rapid connectivity, while Glue is best where AWS integration is the core priority.

Performance and Scalability

Glue automatically scales resources up and down to meet unpredictable data volumes, making it strong for unpredictable, high-scale batch or stream jobs.

Azure Data Factory enables scaling via Integration Runtimes, but requires deliberate configuration and tuning for optimal pipeline performance.

If unpredictable workloads and autoscaling are critical, Glue is designed for that. For controlled scaling with manual oversight, ADF suffices.

Learning Curve

AWS Glue requires proficiency in Spark, Python, or Scala; this technical learning curve can delay onboarding for new team members.

ADF’s easier, visual interface and prebuilt connectors help new users ramp up quickly, making it more accessible across roles.

ADF is preferable when time-to-productivity and ease of use are essential; Glue suits teams already skilled in code-driven ETL.

Cost Predictability

Glue bills based on compute (DPU) time, so cost can vary with data size, but there are controls for resource allocation and alerts.

ADF uses activity-based billing, with charges dependent on use and Integration Runtime choices, affecting cost transparency and predictability.

For clear, fixed costs, review each pricing model closely. Glue can spike with compute, while ADF’s costs depend on pipeline activity.

Data Cataloging

AWS Glue features a centralized Data Catalog that stores metadata, supports schema discovery, and works with Athena, Redshift, and EMR.

ADF integrates with Azure Data Catalog for metadata management, supporting consistent schema and data management across Azure tools.

Glue offers a mature, automated metadata repository for AWS; ADF’s cataloging works best in Azure-centric stacks.

Job Scheduling and Monitoring

AWS Glue supplies a built-in scheduler and monitoring managed via the AWS Console; integration with EventBridge or Lambda enables advanced triggers.

ADF provides powerful scheduling and monitoring with varied triggers, robust tracking, and pipeline-level observability.

ADF is a strong choice for complex scheduling and visibility; Glue covers AWS-triggered events and basic to advanced job monitoring.

Security

Glue implements controls through AWS IAM roles, Virtual Private Cloud (VPC) integration, and encryption at rest and in transit.

ADF secures access and data with Azure Active Directory, Private Link, and Key Vault integration for credential management.

Select Glue for AWS security alignment or ADF for teams invested in Azure’s security standards.

When to Choose AWS Glue?

Choose AWS Glue when you need a serverless, flexible ETL platform tightly coupled to AWS. Consider Glue if:

• Operating primarily within the AWS ecosystem and need services that align natively.
• Your team can code transformation logic using Spark, Python, or Scala.
• Detailed control over complex transformations and ETL jobs is required.
• Handle unpredictable, large-scale, or streaming data workloads with autoscaling.

When to Choose Azure Data Factory?

Azure Data Factory is well-suited for teams valuing visual design and Azure integrations. Consider ADF if:

• Data infrastructure already relies on Azure services and Microsoft’s cloud platform.
• Prefer building ETL pipelines without coding, using a drag-and-drop interface.
• Need broad connector coverage, including hybrid and on-premises sources.
• The team has limited coding capacity but needs effective scheduling and monitoring.

Why does Hevo stand out?

• No code, no maintenance: Hevo offers 150 connectors to ingest data from different sources into their data warehouse without writing a single line of code. Its UI is easy to use. With Hevo, you don’t need to worry about infrastructure, maintenance, or manual intervention.
• Built-in monitoring & alerting: Hevo provides built-in monitoring and robust alerting capabilities. It checks the health and status of pipelines continuously. If any issue arises, schema mismatches, or quota overrides,  it automatically sends an alert to your set-up alert destination.
• Transparent pricing with no surprises: Hevo’s pricing model is transparent, with clear options for monthly or yearly billing and no hidden fees. Pricing is based on plan tiers and usage. 
• Fast setup, 24×7 support: Hevo offers 24×7 customer support through live chat, email, and comprehensive documentation so that your critical production issues are catered to on priority.

Conclusion

We discussed both AWS Glue and Azure Data Factory in detail. They both are powerful integration tools, but they cater to different needs. If your organization has already adopted the AWS ecosystem and requires the serverless option to transform your data, AWS Glue is for you. On the contrary, if you are looking for a low-code ETL tool tightly coupled with Microsoft services, Azure Data Factory is for you.

While both are great tools, teams today are looking for easy-to-set-up, low-code, and maintenance-free ETL services with transparent pricing. This is where Hevo comes to rescue. Hevo offers a code platform with over 150 pre-built connectors that work across cloud and on-prem systems. Sign up for a 14-day free trial with Hevo and experience seamless ETL.

FAQ