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Azure Data Factory vs FME 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
1st
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
89
Ranking in other categories
Cloud Data Warehouse (3rd)
FME
Ranking in Data Integration
27th
Average Rating
8.6
Reviews Sentiment
6.8
Number of Reviews
5
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of January 2025, in the Data Integration category, the mindshare of Azure Data Factory is 10.8%, down from 13.4% compared to the previous year. The mindshare of FME is 2.0%, up from 1.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration
 

Featured Reviews

Joy Maitra - PeerSpot reviewer
Facilitates seamless data pipeline creation with good analytics and and thorough monitoring
Azure Data Factory is a low code, no code platform, which is helpful. It provides many prebuilt functionalities that assist in building data pipelines. Also, it facilitates easy transformation with all required functionalities for analytics. Furthermore, it connects to different sources out-of-the-box, making integration much easier. The monitoring is very thorough, though a more readable version would be appreciable.
Alan Bloor - PeerSpot reviewer
Great for handling large volumes of data, but it is priced a bit high
When I do coding, I think about every single function. Some of these functions can be very elementary, like doing a substring or some capitalization. But FME removes all that coding because it's a transformer, so the time to develop an application to get to a point where you're producing results is decreased massively. It used to take weeks and months to develop software, and now I can use something like FME, and within one day, we get results. We can look at and validate data. We make minor subtle changes to the workbenches to improve it. We can share the workbenches. We don't have to use GitHub or anything else.

Quotes from Members

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

Pros

"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."
"Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data."
"For me, it was that there are dedicated connectors for different targets or sources, different data sources. For example, there is direct connector to Salesforce, Oracle Service Cloud, etcetera, and that was really helpful."
"Powerful but easy-to-use and intuitive."
"The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain."
"It's cloud-based, allowing multiple users to easily access the solution from the office or remote locations. I like that we can set up the security protocols for IP addresses, like allow lists. It's a pretty user-friendly product as well. The interface and build environment where you create pipelines are easy to use. It's straightforward to manage the digital transformation pipelines we build."
"The most important feature is that it can help you do the multi-threading concepts."
"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."
"All spatial features are unrivaled, and the possibility to execute them based on a scheduled trigger, manual, e-mail, Websocket, tweet, file/directory change or virtually any trigger is most valuable."
"It has standard plug-ins available for different data sources."
"It has a very friendly user interface. You don't need to use a lot of code. For us that's the most important aspect about it. Also, it has a lot of connectors and few forms. It has a strong facial aspect. It can do a lot of facial analysis."
"The most valuable feature of FME is the graphical user interface. There is nothing better. It is very easy to debug because you can see all steps where there are failures. Overall the software is easy to optimize a process."
"We make minor subtle changes to the workbenches to improve it. We can share the workbenches. We don't have to use GitHub or anything else."
 

Cons

"Azure Data Factory could benefit from improvements in its monitoring capabilities to provide a more robust feature set. Enhancing the ease of deployment to higher environments within Azure DevOps would be beneficial, as the current process often requires extensive scripting and pipeline development. It is also known for the flexibility of the data flow feature, particularly in supporting more dynamic data-driven architectures. These enhancements would contribute to a more seamless and efficient workflow within GitLab."
"They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas."
"Some known bugs and issues with Azure Data Factory could be rectified."
"It would be better if it had machine learning capabilities."
"For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better."
"You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats."
"When the record fails, it's tough to identify and log."
"The deployment should be easier."
"The one thing that always appears in the community is the ability to make really easy loops to loop through data efficiently. That needs to be added at some point."
"To get a higher rating, it would have to improve the price and the associated scalability. These are the main issues."
"FME's price needs improvement for the African market."
"FME can improve the geographical transformation. I've had some problems with the geographical transformations, but it's probably mostly because I'm not the most skilled geographer in-house. The solution requires some in-depth knowledge to perform some functions."
"Improvements could be made to mapping presentations."
 

Pricing and Cost Advice

"Pricing appears to be reasonable in my opinion."
"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."
"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."
"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."
"Data Factory is expensive."
"The solution's pricing is competitive."
"I would not say that this product is overly expensive."
"Pricing is comparable, it's somewhere in the middle."
"The product's price is reasonable."
"FME Server used to cost £10,000; now it can cost over £100,000."
"We used the standard licensing for our use of FME. The cost was approximately €15,000 annually. We always welcome less expensive solutions, if the solution could be less expensive it would be helpful."
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
12%
Manufacturing Company
9%
Healthcare Company
7%
Government
29%
Energy/Utilities Company
12%
Computer Software Company
8%
Comms Service Provider
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
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 do you like most about FME?
We make minor subtle changes to the workbenches to improve it. We can share the workbenches. We don't have to use GitHub or anything else.
What is your experience regarding pricing and costs for FME?
The pricing is really bad. Last year, they rebranded the whole pricing structure. It used to be moderately priced at about £400 per user per year. Now they've changed the whole thing, and it's expe...
What needs improvement with FME?
The one thing that always appears in the community is the ability to make really easy loops to loop through data efficiently. That needs to be added at some point. There must be a technical or comm...
 

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
Shell, US Department of Commerce, PG&E, BC Hydro, City of Vancouver, Enel, Iowa DoT, San Antonio Water System
Find out what your peers are saying about Azure Data Factory vs. FME and other solutions. Updated: January 2025.
831,265 professionals have used our research since 2012.