Azure Data Factory vs SAS Data Integration Server comparison

Azure Data Factory vs. SAS Data Integration Server

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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

6.7

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (5th)

SAS Data Integration Server

Ranking in Data Integration

41st

Average Rating

7.2

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of June 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.3%, down from 8.1% 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
Product	Mindshare (%)
Azure Data Factory	2.3%
SAS Data Integration Server	0.9%
Other	96.8%

Data Integration

Featured Reviews

KandaswamyMuthukrishnan

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.

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reviewer2937544

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.

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Quotes from Members

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

Pros

"When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit."

"The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted."

"If you have Azure as a cloud service and you want to perform ETL then Azure Data Factory is a product that I can recommend."

"The initial setup is very quick and easy."

"The most valuable feature I have found at Azure Data Factory is the data flow function."

"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."

"The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."

"Azure Data Factory's most valuable features are the packages and the data transformation that it allows us to do, which is more drag and drop, or a visual interface. So, that eases the entire process."

More Azure Data Factory pros

"The most valuable feature of the solution is its amazing capabilities in regard to data handling."

"The solution offers very good data manipulation and loading."

"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 is very stable."

Cons

"Additionally, the ability to handle the largest volumes of data is another concern; if I have to manage more than one terabyte of data every day, I am not comfortable dealing with Azure Data Factory and had to switch to Oracle Data Integrators (ODI) because it lacks performance features."

"To my mind, the solution needs to be more connectable to its own services."

"Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically."

"While it has a range of connectors for various systems, such as ERP systems, the support for these connectors can be lacking."

"In the next release, it's important that some sort of scheduler for running tasks is added."

"When working with AWS, we have noticed that the difference between ADF and AWS is that AWS is more customer-focused. They're more responsive compared to any other company. ADF is not as good as AWS, but it should be. If AWS is ten out of ten, ADF is around eight out of ten. I think AWS is easier to understand from the GUI perspective compared to ADF."

"The pricing model should be more transparent and available online."

"The inability to connect local VMs and local servers into the data flow is a limitation that prevents giving Azure Data Factory a perfect score."

More Azure Data Factory cons

"One area for improvement is the installation process."

"The initial setup of SAS Data Integration Server was complex."

"Something that could improve is writing SQL to integration studio because it is not that straightforward. There should be better integration with other software."

"So I would like to see improved integration with other software."

"The transform tool has limited access. They should make it more flexible."

"The initial setup had issues, and even after using it for about one year, it was still not fixed."

Pricing and Cost Advice

"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."

"The solution is cheap."

"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."

"Azure products generally offer competitive pricing, suitable for diverse budget considerations."

"The price you pay is determined by how much you use it."

"The licensing cost is included in the Synapse."

"Our licensing fees are approximately 15,000 ($150 USD) per month."

"I would not say that this product is overly expensive."

More Azure Data Factory pricing and cost advice

"It is an expensive program."

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Top Industries

By visitors reading reviews

Financial Services Firm

12%

Computer Software Company

Manufacturing Company

Construction Company

Financial Services Firm

26%

Construction Company

13%

Comms Service Provider

11%

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	21
Large Enterprise	63

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?

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...

Snowflake vs Azure Data Factory

Comparisons

Compared 5% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Azure Data Factory

Compared 4% of the time

Databricks vs Azure Data Factory

Compared 4% of the time

Palantir Foundry vs Azure Data Factory

Compared 3% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 3% of the time

More Azure Data Factory Competitors

SSIS vs SAS Data Integration Server

Compared 8% of the time

Oracle Data Integrator (ODI) vs SAS Data Integration Server

Compared 5% of the time

Informatica PowerCenter vs SAS Data Integration Server

Compared 4% of the time

Palantir Foundry vs SAS Data Integration Server

Compared 4% of the time

Axway AMPLIFY Application Integration vs SAS Data Integration Server

Compared 4% of the time

More SAS Data Integration Server Competitors

Product Reports

Azure Data Factory

Download Azure Data Factory product report

Data Integration

Download SAS Data Integration Server product report

Also Known As

No data available

SAS Enterprise Data Integration Server, Enterprise Data Integration Server

Overview

Azure Data Factory efficiently manages and integrates data from various sources, enabling seamless movement and transformation across platforms. Its valuable features include seamless integration with Azure services, handling large data volumes, flexible transformation, user-friendly interface, extensive connectors, and scalability. Users have experienced improved team performance, workflow simplification, enhanced collaboration, streamlined processes, and boosted productivity.

Microsoft

SAS Data Integration Server automates and manages data integration processes, enabling efficient data management, transformation, and migration in enterprises. It provides a comprehensive suite for data-driven solutions.

Designed for extensive data management needs, SAS Data Integration Server supports seamless data integration, allowing users to access, run, and monitor data transformation processes. It is tailored to enhance data quality and streamline processes, serving industries reliant on precise data interpretation and utilization. With robust functionalities, it simplifies complex data environments, promoting productivity and reliability in data handling.

What are the key features of SAS Data Integration Server?

Data Profiling: Ensures data accuracy and quality through thorough analysis.
ETL Capabilities: Extracts, transforms, and loads data efficiently.
Metadata Management: Centralized control over data definitions and usage.
Data Lineage: Tracks data source origins and transformations for transparency.

What benefits and ROI should reviewers look for?

Improved Data Accuracy: Enhances decision-making with high-quality data.
Increased Efficiency: Reduces time and effort in data-related tasks.
Scalability: Adapts to growing business data demands seamlessly.
Reduced Costs: Optimizes resources, minimizing unnecessary expenditures.

SAS Data Integration Server finds applications across numerous industries, like finance and healthcare, where data integration and quality are critical. In finance, it helps manage vast amounts of transaction data, while in healthcare, it ensures accurate patient information management and reporting.

SAS

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

Azure Data Factory vs. SAS Data Integration Server