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

A significant amount of market cloud infrastructure is spread between the top 3 cloud service providers, AWS, Azure, and GCP. However, when we talk about implementing data infrastructures, AWS and Azure are generally considered the leading options. Both services provide great support for data engineering tools. 

AWS Glue and Azure Data Factory are top-tier ETL tools that have been trusted and adopted widely. While Glue is serverless and offers deep integration with AWS services, ADF is known for its intuitive UI and seamless Azure ecosystem support. Both tools are great for implementing data pipelines; they have their own set of complexities, control, use cases, and pricing.

Provided that each one is best in their way, choosing between both can be daunting. This guide analyzes AWS Glue vs Azure Data Factory – its capability, use case, and pricing to help you pick the right tool for your needs.

By the end of this article, you’ll know exactly when to use AWS Glue, when Azure Data Factory makes more sense, and how Hevo might be the smarter alternative.

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 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, autoscaling compute: AWS Glue Spark jobs automatically scale workers based on real-time workload demands using auto scaling. You can also control how many workers you want for the job to go, to control your costs. This dynamic resource allocation:
  • Optimizes job performance while minimizing costs
  • Handles data volume fluctuations without manual intervention
  • Supports both batch and streaming workloads
• Python and Scala support for custom scripts: Glue natively supports PySpark and Scala for ETL scripting. You can write custom transformations and business logic to massage your data according to your needs.
• Built-in scheduler and triggers: Glue provides native triggers for workflow automation. For complex scheduling needs, it can also be integrated with:
  • Amazon EventBridge for cron-based and rate-based scheduling
  • AWS Lambda for event-driven execution patterns
  • Built-in job bookmarks for incremental data processing
• Metadata management via AWS Glue Data Catalog: The AWS Glue Data Catalog serves as a centralized metadata repository with:
  • Automatic schema discovery through crawlers
  • Manual table definition capabilities
  • Cross-service compatibility (works with Athena, EMR, Redshift)
  • Versioning and schema evolution tracking
• Support for Change Data Capture (CDC):  CDC is not natively implemented in Glue, but it can be implemented through:
  • Integration with database transaction logs
  • Time-based windowing for incremental loads
  • Custom ETL logic using job bookmarks

Use cases

Let’s discuss some use cases where AWS Glue can be used.

• ETL for data lakes on AWS:  AWS Glue simplifies complex data integration with glue jobs for transforming data, crawlers to auto-discover schemas and partition structures in S3 and Data Catalog integration, enabling cross-service metadata access (Athena, EMR, Redshift)

It can be used for:

• Multi-source unification: Combine CRM, IoT, and ERP data into partitioned Parquet/ORC files in S3.
• CDC replication: Use Glue bookmarks with database transaction logs for incremental warehouse updates
• Cost optimization: Convert raw JSON/XML to columnar formats pre-querying to reduce querying cost on your big data.
• Real-time event stream processing: Glue allows you to set up Spark streaming jobs that can be used to process events in real time. You can connect to Kinesis, Kafka, or Timestream databases to achieve this.
  • It can be used to perform window-level aggregations on streaming data.
  • Transform streaming data and update in DBs
    

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.

ADF 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 low-code pipeline builder: ADF offers a visual, drag-and-drop interface that can be used to design, configure, and deploy data pipelines without writing code. This makes it easy for both technical and non-technical users to orchestrate complex data workflows quickly.
• Native integration with the Microsoft ecosystem: ADF is tightly integrated with the Microsoft Azure ecosystem. It is easy to set up connections with Microsoft services like Azure Synapse Analytics, Azure SQL Database, Azure Data Lake, Azure Machine Learning, and Power BI natively.
• Over 90 prebuilt connectors: ADF provides a vast library of prebuilt connectors. Prebuilt connectors include connections to databases, file systems, SaaS applications, and generic protocols. You can easily set up these connectors without writing any code to connect your data source.
• Parameterized pipelines and expressions: Pipelines in ADF can be parameterized. This can help you set up the dynamic configuration for different environments based on your use cases. 
• Triggers: ADF supports multiple trigger types
  • Schedule triggers: Run pipelines on a defined schedule (e.g., hourly, daily, weekly).
  • Tumbling window triggers: Process data in fixed-size, non-overlapping time windows. This is ideal for historical or time-sliced data processing.
  • Event-based triggers: Start pipelines in response to events. For example, when a new file lands in the storage
• Hybrid data integration support: ADF can extract and transform data between on-premises systems and cloud services. It allows secure data movement across network boundaries, supporting hybrid and multi-cloud architectures.

Use cases

Let’s discuss some use cases where Azure Data Factory can be used.

• 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. For example, from a healthcare device, data movement can be orchestrated and transformed to enable analytics on clean data.
• Data migration from on-premises to Azure: Businesses thinking of moving from their on-premises system to Azure can use ADF. It facilitates large-scale migrations to Azure cloud storage or databases.
• BI integration with Power BI: ADF pipelines can prepare and deliver analytics-ready data directly to Power BI datasets. This integration can help business intelligence teams to visualize and analyze data efficiently within the Microsoft ecosystem.
  

AWS Glue vs Azure Data Factory vs Hevo: Comparison Table

Now that we have discussed AWS Glue and Azure Data Factory, let’s see a quick comparison of what each has to offer.

Feature	AWS Glue	Azure Data Factory	Hevo
Type	Serverless ETL Service	Hybrid Data Integration	No-code, Real-time Data Pipeline
UI	Script & basic visual	Visual drag-and-drop	Fully visual
Code-free option	Partial	Yes	Yes
Native CDC Support	Yes	Limited	Yes (out-of-the-box)
Prebuilt Connectors	60+	90+	150+
Scheduling & Triggers	Built-in	Advanced	Yes
Pricing Model	Pay-as-you-go (compute time)	Pay-per-activity	Transparent & usage-based
Learning Curve	Steep (requires dev skills)	Moderate	Very Low
Ideal For	Dev-heavy teams on AWS	MS Azure-centric teams	Teams looking for ease & reliability

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

Code vs No-Code

• Glue is great for custom transformations. It is a code-heavy tool.  It also has the basic visual option to create an ETL
• ADF is a no-code-friendly ETL tool. With pre-built connectors, you do not need to write code to connect to data sources. While it offers prebuilt transformation for ease, it also allows advanced customization via Data Flows and expressions.

Connector Ecosystem

• ADF supports 90+ connectors out of the box for Hybrid environments. Its drag-and-drop, easy-to-use interface allows you to quickly connect connectors and set up ETL pipelines.
• Glue covers most AWS-native and popular data sources but often needs custom work for SaaS apps. It offers 60 + connectors for easy data source connections

Performance & Scalability

• Glue automatically scales compute resources. It is ideal for big data processing where volumes can not be predicted.
• ADF offers scaling via Integration Runtimes, but requires manual tuning.

Learning Curve

• Working with Glue requires development knowledge with Python, Scala, and Spark. Its learning curve is comparatively steep.
• With pre-built connector support and no code interface, it is comparatively easy to use and has a moderate learning curve.

Cost Predictability

• Glue charges for data processing time(In DPU’s), which can spike costs. Though you can set alerts or set a limit on the maximum compute resources you would want to use.
• ADF pricing is activity-based, but may add costs via Azure IRs.

Data Cataloging

• AWS Glue includes a centralized Glue data catalog for metadata management and discovery. These catalogs can be generated automatically with crawlers to detect new data sources and schemas.
• Azure Data Factory integrates well with Azure Data Catalog for metadata management.

Job Scheduling & Monitoring

• AWS Glue has a built-in scheduler for ETL jobs. It can also be integrated with EventBridge or triggered from Lambda. Glue provides monitoring via the AWS Management Console.
• Azure Data Factory provides robust scheduling with triggers and monitoring with detailed pipeline tracking.

Security

• AWS Glue uses AWS IAM, VPC integration, and encryption for security.
• Azure Data Factory uses  Azure Active Directory, Private Link, and Azure Key Vault for secure access and data protection

When to choose AWS Glue?

While both the tools provide great features, you could choose Glue when

• You’re deeply embedded in the AWS ecosystem.
• Your team is proficient with Spark, Python, or Scala.
• You need full control over ETL logic and transformations.
• You work with massive data volumes and require autoscaling.
  

When to choose Azure Data Factory?

Azure Data Factory offers a great low-code, drag-and-drop UI. Your team can choose ADF when

• Your infrastructure is Azure-native.
• You prefer a drag-and-drop interface over scripting.
• You need broad connector support, including hybrid sources.
• Your team has less development bandwidth.

Why does Hevo stand out?

Hevo is a no-code data integration tool. It offers 150+ prebuilt connectors, real-time CDC support, and automated schema handling. Hevo simplifies ETL operations across cloud and on-prem systems.

Why choose Hevo?

• 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 in detail both AWS Glue and Azure Data Factory. 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.

At the end, the best choice depends on the tech stack you use, your team’s skill set, and the level of complexity your use case has. If simplicity, speed, and reliability are on top of your mind, Hevo might just be the smarter alternative.

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