Choosing Between SSIS vs ADF: What You Need to Know

By:   |   Updated: 2024-03-12   |   Comments (2)   |   Related: 1 | 2 | 3 | > Azure Data Factory


In the world of data integration and Extract, Transform, Load (ETL) processes, two prominent tools often come into discussion: SQL Server Integration Services (SSIS) and Azure Data Factory (ADF). Both are quite strong but extremely different in form and scope of capabilities. How do you choose which tool is the best for your organization?


Microsoft's SSIS and ADF are both ETL tools mainly used to extract data from one or more sources, transform it into a unified flow, where multiple mapping steps can occur between source files, and finally, load the transformed information onto its destination.

While SSIS was initially developed as a "pure" ETL tool, ADF was introduced after a 10-year gap. Since its launch, ADF was developed primarily as an ELT tool. It was more centered on data flow and choreography. With time, ADF has significantly evolved into a product that efficiently caters to ETL scenarios, and, most notably, its data flow feature guarantees an improved performance regarding data transformation and integration.

Even though ADF is the newer technology, SSIS still has many benefits. How do you choose between the two? Let's look at an example.

Example Scenario

RetailMax is a retail chain with more than 100 stores worldwide. Its operating method uses data regarding sales figures, inventory levels, customer needs, and supply chain logistics, among other matters, to make reliable decisions. Currently, RetailMax uses multiple data formats with different storage locations, including SQL databases for on-premises environments, cloud storage (like Azure Blob Storage), and third-party CRM systems.

Challenges encountered include:

  • Data Distribution: Data from RetailMax is distributed among several systems and formats, which leads to the absence of a unified structure.
  • Reporting: The company needs a reporting feature that generates reports in real-time to respond promptly to changes in the market.
  • Analytics: Analytics requires an effective use of data obtained from various sources, which should be appropriately processed and converted.
  • Growth Potential: As RetailMax grows, the solution must be scalable and adaptive to accommodate this development.
  • Shift to the Cloud: RetailMax is shifting towards a cloud infrastructure to improve scalability and allow remote data access.

RetailMax is currently using SSIS, which provides excellent agility in data integration and transformation and is vital for utilizing their SQL databases within the IT organization. It is a highly user-friendly environment and a robust toolkit for complex ETL processes. The It team sees it as a huge positive as it abbreviates the learning curve and enhances launch speed. This method also improves RetailMax's data management and analytical skills, which are essential for the current stage of operational development.

But when RetailMax shifts toward cloud-based data management, ADF becomes the better alternative. Its flawless compatibility with various applications on Microsoft's cloud environment and its scalability, especially when managing real-time data processing and analytics, make it inevitable. The move to ADF is paramount for RetailMax, particularly because the company can benefit fully from cloud capabilities centered on data-driven insights and efficient operations.

Considerations When Choosing Between SSIS and ADF

There are many factors to consider when deciding between these two tools.

Environment Compatibility

Typically, SSIS is powerful in on-premises setups, providing capable data integration solutions primarily within an organization's environment. Although it allows for cloud integration functionality, this is less extensive than its capabilities as an on-premises system.

However, ADF was developed specifically for cloud environments. Perhaps this mainly supports data integration across different cloud services, but it also supports on-premises sources. Note: ADF is a great choice because it offers a smooth transition between on-premises and cloud data.

Data Handling Capabilities

As for data handling capacities, both SSIS and ADF demonstrate unique advantages that can be applied in various contexts. Regarding managing sophisticated data integrations, SSIS really shines when it comes to dealing with on-premises systems that handle structured data very well. Nevertheless, it requires more effort to work with unstructured data and focuses primarily on batch processing; this means little capabilities in streaming data.

On the other hand, ADF shows strong capabilities in coordinating and controlling data flows through cloud-based platforms. It is good at processing structured and unstructured data, meaning it can be used in different data situations. In addition, ADF enables not only batch processing but also streaming data that fits perfectly in real-time analytics and allows easier management of customary cloud structures.

Extensibility with SDKs

Although SSIS provides some SDK support, it is relatively limited as it mainly concentrates on .NET and SQL Server. Therefore, it is a perfect choice for organizations with a deep presence in the Microsoft environment.

ADF, on the other hand, provides a wide range of SDK support, including Azure. This unlimited support paves the way for seamless integration with a broad scope of Azure services, boosting its value as an effective instrument for cloud-based and hybrid systems.


Regarding its performance, SSIS excels at processing immense amounts of data, primarily when implemented as an on-premises setup. It is considered a good performer in the classical data warehouse context.

In the cloud, ADF is scalable. It is possible to change the performance of ADF depending on the service tier, allowing flexibility and scalability in accordance with workload.


Tithe use of SSIS is also related to SQL Server licensing, and a scaling infrastructure can be costly.

For cloud-native solutions, ADF works on a "pay for what you use" basis, a more cost-effective approach. However, prices may fluctuate according to consumption, infrastructure complicity, and services required.


SSIS has a traditional ETL tool architecture, which is better for on-premises data warehouse architectures. ADF, on the other hand, is based on modern cloud-native architecture and aims to integrate with various available cloud services as well as big data systems. This architectural difference allows ADF to better match the changing data integration requirements in cloud-based settings.

SSIS Compared to ADF Table

The table below presents a summarized comparison of key characteristics and functionalities that distinguish these two technologies:

Feature / Aspect SQL Server Integration Services Azure Data Factory
Environment Compatibility Strong on-premises support. Limited but possible cloud integration. Mainly cloud-based and compatible with on-premises integration.
Data Handling Capabilities Excellent for complex integrations, particularly in on-premises situations. More applicable to structured information; more work is necessary for unstructured data. Primarily designed for batch processing. Limited streaming capabilities. Capable of organizing and directing cloud data workflows. Manages organized and unorganized types of data effectively. It can process batch processes and streaming data, making it suitable for real-time analytics.
Interface Design and Usability Better control for the ETL processes; provided in SSIS Designer through Visual Studio. Simplistic user interface, which is browser-based and integrates smoothly with other Azure services.
Extensibility with SDKs Limited to .NET and SQL Server environments. Better SDK support, especially console Azure SDKS, to provide easier integration with other AI as a service offerings.
Performance High performance in on-premises and large-volume data scenarios. Scalable performance, especially in cloud environments; performance depends on the chosen service tier.
Operational Cost Licensing costs associated with SQL Server. Additional costs for scaling the infrastructure. Pay-as-you-go model; cost-effective for cloud-native solutions but can vary based on usage and pipeline complexity.
Architecture Traditional ETL tool, more suited to on-premises data warehouse architectures. Modern, cloud-native ETL/ELT architecture; designed for integration with various cloud services and big data systems.


For our retail company example, the decision between SSIS and ADF should be based on where most of their data resides and their plans for migration to the cloud. SSIS may be more suitable if its current infrastructure is heavily on-premises and highly integrated. But if they attempt to take advantage of cloud analytics and anticipate that their volumes will vary, ADF will be a better option. In some cases, a hybrid approach might be best, i.e., using SSIS for on-premises data and ADF for the cloud.

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About the author
MSSQLTips author Amira Bedhiafi Amira Bedhiafi utilizes Azure Analysis Services to harness the power of scalable, cloud-based OLAP cubes for advanced data analysis and multidimensional modeling.

This author pledges the content of this article is based on professional experience and not AI generated.

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Article Last Updated: 2024-03-12

Comments For This Article

Thursday, March 14, 2024 - 4:17:34 PM - Amira Bedhiafi Back To Top (92072)
Hello Koen !
Thank you for reading the article.
You're correct in saying that ADF is more oriented towards batch data processing rather than real-time streaming. However, it can handle streaming data indirectly through integration with other Azure services that are designed for real-time data processing like Azure Event Hub or Azure Stream Analytics.
When ADF is used with these services, the cost may become a concern for scenarios that process streaming data. Daily repetition of ADF pipelines to process the part of data, that changes constantly for massive amounts of data is quite expensive.

Wednesday, March 13, 2024 - 5:25:08 AM - Koen Verbeeck Back To Top (92064)
Hi Amira,
how does ADF handle streaming data? I was under the impression that ADF is a more batch-oriented solution and is in many cases really expensive if you want to use it for streaming (at least if you run a pipeline many times a day).


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