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Azure Data Factory vs SAS Data Integration Server comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 19, 2024

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Azure Data Factory
Ranking in Data Integration
4th
Average Rating
8.0
Reviews Sentiment
6.8
Number of Reviews
94
Ranking in other categories
Cloud Data Warehouse (5th)
SAS Data Integration Server
Ranking in Data Integration
40th
Average Rating
7.2
Reviews Sentiment
6.5
Number of Reviews
4
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of May 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.4%, down from 8.6% compared to the previous year. The mindshare of SAS Data Integration Server is 0.9%, up from 0.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration Mindshare Distribution
ProductMindshare (%)
Azure Data Factory2.4%
SAS Data Integration Server0.9%
Other96.7%
Data Integration
 

Featured Reviews

KandaswamyMuthukrishnan - PeerSpot reviewer
Director at a computer software company with 1,001-5,000 employees
Integrates diverse data sources and streamlines ETL processes effectively
Regarding potential areas of improvement for Azure Data Factory, there is a need for better data transformation, especially since many people are now depending on DataBricks more for connectivity and data integration. Azure Data Factory should consider how to enhance integration or filtering for more transformations, such as integrating with Spark clusters. I am satisfied with Azure Data Factory so far, but I suggest integrating some AI functionality to analyze data during the transition itself, providing insights such as null records, common records, and duplicates without running a separate pipeline or job. The monitoring tools in Azure Data Factory are helpful for optimizing data pipelines; while the current feature is adequate, they can improve by creating a live dashboard to see the online process, including how much percentage has been completed, which will be very helpful for people who are monitoring the pipeline.
NN
Works at a financial services firm with 5,001-10,000 employees
Offloads processes on the server side but needs better installation syntax
One area for improvement is the installation process. Another point could be the syntax, as it sometimes involves using syntax names that are not intuitive. For example, to calculate the difference between two dates, the general syntax in SAS is called the data difference or data net function. However, another name is used, such as NF and INK. Without knowledge of SAS programming, it becomes unclear what these functions mean. It is not good to define function names this way.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"The most valuable features of the solution are its ease of use and the readily available adapters for connecting with various sources."
"From my experience so far, the best feature is the ability to copy data to any environment."
"Data Factory allows you to pull data from multiple systems, transform it according to your business needs, and load it into a data warehouse or data lake."
"The reason that we implemented this product is for the full integration with the whole Azure environment."
"The data flows were beneficial, allowing us to perform multiple transformations."
"The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem."
"Microsoft's technical support is responsive and quick to help."
"I think it makes it very easy to understand what data flow is and so on. You can leverage the user interface to do the different data flows, and it's great. I like it a lot."
"A key feature allows us to enhance job performance by offloading processing to the server side, rather than processing on the server itself."
"The most valuable feature of the solution is its amazing capabilities in regard to data handling."
"The solution is very stable."
"A key feature allows us to enhance job performance by offloading processing to the server side, rather than processing on the server itself."
"A key feature allows us to enhance job performance by offloading processing to the server side, rather than processing on the server itself."
"The solution offers very good data manipulation and loading."
"The solution offers very good data manipulation and loading."
"The solution is very stable."
 

Cons

"Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."
"It can improve from the perspective of active logging. It can provide active logging information."
"Understanding the pricing model for Data Factory is quite complex. It needs to be simplified, and easier to understand."
"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities."
"Some of the optimization techniques are not scalable."
"It would be helpful if they could adjust the data capture feature so that when there are source-side changes ADF could automatically figure it out."
"DataStage is easier to learn than Data Factory because it's more visual."
"Real-time replication is required, and this is not a simple task."
"The initial setup had issues, and even after using it for about one year, it was still not fixed."
"The transform tool has limited access. They should make it more flexible."
"One area for improvement is the installation process."
"The transform tool has limited access. They should make it more flexible."
"The initial setup of SAS Data Integration Server was complex."
"So I would like to see improved integration with other software."
"Something that could improve is writing SQL to integration studio because it is not that straightforward. There should be better integration with other software."
"The initial setup had issues, and even after using it for about one year, it was still not fixed."
 

Pricing and Cost Advice

"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."
"Pricing is comparable, it's somewhere in the middle."
"ADF is cheaper compared to AWS."
"I don't see a cost; it appears to be included in general support."
"In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal."
"The cost is based on the amount of data sets that we are ingesting."
"The pricing is a bit on the higher end."
"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."
"It is an expensive program."
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Top Industries

By visitors reading reviews
Financial Services Firm
12%
Computer Software Company
10%
Manufacturing Company
9%
Government
6%
Financial Services Firm
26%
Construction Company
11%
Comms Service Provider
10%
Computer Software Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise20
Large Enterprise57
No data available
 

Questions from the Community

How do you select the right cloud ETL tool?
AWS Glue and Azure Data factory for ELT best performance cloud services.
How does Azure Data Factory compare with Informatica PowerCenter?
Azure Data Factory is flexible, modular, and works well. In terms of cost, it is not too pricey. It offers the stability and reliability I am looking for, good scalability, and is easy to set up an...
How does Azure Data Factory compare with Informatica Cloud Data Integration?
Azure Data Factory is a solid product offering many transformation functions; It has pre-load and post-load transformations, allowing users to apply transformations either in code by using Power Q...
What needs improvement with SAS Data Integration Server?
One area for improvement is the installation process. Another point could be the syntax, as it sometimes involves using syntax names that are not intuitive. For example, to calculate the difference...
What is your primary use case for SAS Data Integration Server?
I am involved in the ETR job. My role is focused on executing the ETR job.
What advice do you have for others considering SAS Data Integration Server?
I use it without further details. For example, if I use SAS to connect to a NetEazt database or purchase a shared asset to ODBC, I can connect to any database with ODBC connection support. The over...
 

Also Known As

No data available
SAS Enterprise Data Integration Server, Enterprise Data Integration Server
 

Overview

 

Sample Customers

1. Adobe 2. BMW 3. Coca-Cola 4. General Electric 5. Johnson & Johnson 6. LinkedIn 7. Mastercard 8. Nestle 9. Pfizer 10. Samsung 11. Siemens 12. Toyota 13. Unilever 14. Verizon 15. Walmart 16. Accenture 17. American Express 18. AT&T 19. Bank of America 20. Cisco 21. Deloitte 22. ExxonMobil 23. Ford 24. General Motors 25. IBM 26. JPMorgan Chase 27. Microsoft (Azure Data Factory is developed by Microsoft) 28. Oracle 29. Procter & Gamble 30. Salesforce 31. Shell 32. Visa
Credit Guarantee Corporation, Cr_dito y Cauci‹n, Delaware State Police, Deutsche Lufthansa, Directorate of Economics and Statistics, DSM, Livzon Pharmaceutical Group, Los Angeles County, Miami Herald Media Company, Netherlands Enterprise Agency, New Zealand Ministry of Health, Nippon Paper, West Midlands Police, XS Inc., Zenith Insurance
Find out what your peers are saying about Azure Data Factory vs. SAS Data Integration Server and other solutions. Updated: April 2026.
893,244 professionals have used our research since 2012.