Introduction
Data integration is a key part of modern analytics and automation. Microsoft provides two main ETL tools for this purpose: SSIS and Azure-SSIS IR in Azure Data Factory (ADF).
SSIS is a long-standing on-premises ETL solution, while ADF is a cloud-native orchestration and transformation platform.
Both tools can extract, transform, and load data effectively, but they differ significantly in deployment model, scalability, maintenance requirements, and overall cost.
What is SSIS
SQL Server Integration Services (SSIS) is Microsoft’s on-premises ETL platform for extracting, transforming, and loading data. It runs locally or on virtual machines and integrates with SQL Server and many other systems.
Advantages of SSIS
- Full control and customization: You manage the entire environment, including the operating system, updates, and configuration.
- Rich transformation capabilities: SSIS includes many built-in components, and you can extend its capabilities using third-party add-ons such as ZappySys SSIS PowerPack. It offers components like the REST API Task, JSON Source, JSON Destination, and API Connectors.
- Predictable cost: Infrastructure costs remain fixed once your system is deployed.
- High performance: Optimized SSIS packages can process large data volumes efficiently.
Disadvantages of SSIS
- Maintenance overhead: You must maintain servers, apply updates, and handle scaling manually.
- Manual scalability: Scaling requires adding new servers or VMs.
- Licensing cost: SQL Server and Windows Server licenses can significantly increase the total cost of ownership.
What is Azure Data Factory (ADF)
Azure Data Factory (ADF) is Microsoft’s fully managed, cloud-based ETL and orchestration service. It allows you to build, schedule, and monitor data pipelines that move and transform data across cloud and on-premises environments.
When migrating SSIS to the cloud, ADF can run SSIS packages using the Azure-SSIS Integration Runtime (IR), a managed compute environment for executing SSIS workloads within Azure.
For detailed pricing information, see Microsoft’s Azure Data Factory pricing guide.
Advantages of Azure-SSIS IR
- Reduced cost: Previously, running SSIS required a full SQL Server license. With Azure Data Factory, you can execute ETL workloads without purchasing SQL Server at a fraction of the cost.
- Fully managed environment: Microsoft handles runtime management, patching, and updates.
- Elastic scalability: You can scale Integration Runtime nodes up or down as needed, or stop them when idle to save costs.
- Pay-as-you-go model: You only pay for compute time and data movement while pipelines are active.
- Hybrid and cloud connectivity: Works seamlessly with Azure SQL, Synapse Analytics, Databricks, and APIs.
- Easier migration: Existing SSIS packages can be lifted and shifted to Azure with minimal modification.
Disadvantages of Azure-SSIS IR
- No direct OS access: You cannot access the operating system via RDP; custom components must be installed using setup scripts.
- Potentially higher costs for continuous workloads: Always-on environments may cost more than on-prem if not optimized.
- Limited scripting flexibility: Advanced transformations may require Databricks or custom activities.
- Learning curve: Developers must understand new ADF concepts, including linked services, triggers, and data flows.
It’s possible to automatically stop Azure-SSIS IR after running SSIS package, check ADF tutorial: How to schedule Azure-SSIS IR start and stop article for more info.
SSIS vs Azure Data Factory: Feature Comparison
| Feature | SSIS (On-Prem / VM) | Azure Data Factory (Cloud / Azure-SSIS IR) |
|---|---|---|
| Deployment | On-premises or hosted VM | Managed Azure Integration Runtime |
| Maintenance | Manual patching and monitoring | Fully managed by Microsoft |
| Scalability | Manual (add more VMs) | Elastic; adjustable node count and size |
| Cost model | Fixed infrastructure | Pay-as-you-go |
| Custom components | Installed directly | Installed through setup script |
| Security | Full OS and network access | VNet integration; no OS access |
| Integration | SQL Server and local data | Cloud, hybrid, and APIs |
| ZappySys PowerPack support | Installed manually | Supported via setup script |
| Best suited for | Always-on workloads | Variable or cloud workloads |
ZappySys SSIS PowerPack environments
ZappySys PowerPack enhances both SSIS and Azure-SSIS through:
- REST API Task, JSON Source, SFTP Task, API Connectors, and other components
- Native support for APIs, cloud storage, and web services
- Compatibility with Azure-SSIS IR using custom setup scripts and automated license activation
For details, see the complete guide on how to run SSIS in Azure Data Factory.
Also, discover Azure-SSIS IR integration with different API and file connectors in ZappySys API Integration Hub.
ZappySys SSIS PowerPack prices
| License Type | Standard | Professional | Enterprise |
|---|---|---|---|
| Annual Subscription | $1,499/year | $1,899/year | $2,299/year |
| Perpetual License | $3,699 one-time | $4,999 one-time | $5,999 one-time |
| Annual Support Renewal (for perpetual) | $799/year | $999/year | $1,199/year |
Each ZappySys PowerPack license includes:
- Access to software updates and feature enhancements
- Unlimited support via email, chat, and screen-sharing sessions
- Eligibility for discounted product upgrades
For details, visit the ZappySys purchase page.
Choose SSIS if
- Your workloads are constant and resource-intensive
- You need complete control over your environment and network
- You already own SQL Server infrastructure
Choose Azure Data Factory if
- You want a managed, scalable cloud service
- Your workloads vary or are seasonal
- You plan to modernize or migrate ETL processes to Azure
Conclusion
SSIS and Azure Data Factory share the same goal—data integration—but differ in how they operate and how they are billed.
SSIS offers control, performance, and predictable costs for continuous workloads, while Azure Data Factory provides flexibility, scalability, and reduced maintenance for dynamic, cloud-based environments.
With ZappySys PowerPack, both platforms gain the same advanced capabilities for REST APIs, JSON, XML, and data integration, ensuring consistent functionality no matter where your ETL pipelines run.