Azure Data Factory vs FME comparison

Microsoft and Safe Software are both solutions in the Data Integration category. Microsoft is ranked #1 with an average rating of 8.2, while Safe Software is ranked #23 with an average rating of 7.5. Microsoft holds a 5.2% mindshare in DI, compared to Safe Software’s 1.6% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 100% of Safe Software users who would recommend it.

Azure Data Factory

Read 92 Azure Data Factory reviews

13,131 Views
9,192 Comparison Views

92% willing to recommend

FME

Read 7 FME reviews

2,587 Views
2,018 Comparison Views

100% willing to recommend

Azure Data Factory

FME

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Executive SummaryUpdated on Dec 19, 2024

FME and Azure Data Factory are prominent contenders in the data integration and automation field. While Azure Data Factory is often deemed superior due to its comprehensive native integrations and scalability, FME gains attention for its more attractive pricing options.

Features: FME is recognized for its spatial data transformation capabilities and flexibility with different data formats, offering an intuitive graphical user interface and robust support for spatial analysis. Azure Data Factory is strong in data orchestration and integration with excellent cloud and hybrid support, providing a seamless ETL experience with over 100 built-in connectors, data transformation capabilities, and powerful pipeline management.

Room for Improvement: FME could improve its scalability options and enhance its cloud integration features to compete better with larger data solutions. Additionally, supporting more advanced data orchestration tools would be beneficial. Azure Data Factory might focus on simplifying its complex deployment process, enhancing support for varied data sources, and expanding its customer service offerings to be more personalized.

Ease of Deployment and Customer Service: FME offers a simpler deployment process enhanced by comprehensive support services, which ease the implementation for users. It is known for being user-friendly, providing quick setup options. Azure Data Factory, while having a more intricate deployment process, benefits from its strong cloud-native support system which is ideal for handling complex enterprise-level deployments, offering solid backend support but requiring significant expertise for initial setup.

Pricing and ROI: FME generally provides lower upfront costs, appealing to businesses seeking advanced data transformation without substantial investment. Its cost-effective nature makes it suitable for smaller companies. In contrast, Azure Data Factory's pricing aligns with its broad feature set, often necessitating higher initial investment but promising a higher return on investment for large enterprises with demands for extensive scalability and robust cloud infrastructure.

To learn more, read our detailed Azure Data Factory vs. FME Report (Updated: September 2025).

Buyer's Guide

Azure Data Factory vs. FME

September 2025

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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

1st

Average Rating

8.0

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (2nd)

FME

Ranking in Data Integration

23rd

Average Rating

8.6

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of October 2025, in the Data Integration category, the mindshare of Azure Data Factory is 5.2%, down from 11.0% compared to the previous year. The mindshare of FME is 1.6%, down from 1.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Market Share Distribution
Product	Market Share (%)
Azure Data Factory	5.2%
FME	1.6%
Other	93.2%

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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Alan Bloor

Geographic Information System Developer at Public Sector

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.

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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

"It is a complete ETL Solution."

"An excellent tool for pipeline orchestration."

"This solution will allow the organisation to improve its existing data offerings over time by adding predictive analytics, data sharing via APIs and other enhancements readily."

"The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable."

"In terms of my personal experience, it works fine."

"The solution handles large volumes of data very well. One of its best features is its ability to integrate data end-to-end, from pulling data from the source to accessing Databricks. This makes it quite useful for our needs."

"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."

"Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations."

More Azure Data Factory pros

"From my reseller perspective, the best features in FME are the ease of operation and the fact that it works."

"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."

"FME is spatially aware and understands how to deal with the conversion of spatial objects and their attributes."

"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."

"It has standard plug-ins available for different data sources."

"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."

Cons

"There is a need for better data transformation, especially since many people are now depending on DataBricks more for connectivity and data integration."

"There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run."

"The user interface could use improvement. It's not a major issue but it's something that can be improved."

"The main challenge with implementing Azure Data Factory is that it processes data in batches, not near real-time. To achieve near real-time processing, we need to schedule updates more frequently, which can be an issue. Its interface needs to be lighter."

"There is always room to improve. There should be good examples of use that, of course, customers aren't always willing to share. It is Catch-22. It would help the user base if everybody had really good examples of deployments that worked, but when you ask people to put out their good deployments, which also includes me, you usually got, "No, I'm not going to do that." They don't have enough good examples. Microsoft probably just needs to pay one of their partners to build 20 or 30 examples of functional Data Factories and then share them as a user base."

"There is room for improvement primarily in its streaming capabilities. For structured streaming and machine learning model implementation within an ETL process, it lags behind tools like Informatica."

"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."

"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."

More Azure Data Factory cons

"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."

"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."

"We are looking at the possibility of using Glue instead of FME, using the native AWS product."

Pricing and Cost Advice

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

"The pricing is a bit on the higher end."

"Data Factory is affordable."

"It's not particularly expensive."

"The price is fair."

"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."

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

"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."

More Azure Data Factory pricing and cost advice

"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."

"The product's price is reasonable."

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Top Industries

By visitors reading reviews

Financial Services Firm

13%

Computer Software Company

12%

Manufacturing Company

Government

35%

Energy/Utilities Company

12%

Computer Software Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	19
Large Enterprise	55

By reviewers
Company Size	Count
Small Business	2
Midsize Enterprise	1
Large Enterprise	4

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.

See all answers

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...

See all answers

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...

See all answers

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.

See all answers

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...

See all answers

What needs improvement with FME?

I haven't had any input or requirements from any customers that are not currently covered, so I don't have any additional needs that were identified or raised to me. Regarding pricing, with the mod...

See all answers

Comparisons

Snowflake vs Azure Data Factory

Compared 8% of the time

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

Compared 6% of the time

Informatica PowerCenter vs Azure Data Factory

Compared 6% of the time

Palantir Foundry vs Azure Data Factory

Compared 5% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 5% of the time

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Compared 10% of the time

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Alteryx Designer vs FME

Compared 9% of the time

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Compared 7% of the time

Boomi iPaaS vs FME

Compared 6% of the time

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Product Reports

Buyer's Guide

Azure Data Factory

October 2025

Download Azure Data Factory product report

Buyer's Guide

FME

October 2025

Download FME product report

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

FME is the data integration platform with the best support for spatial data. Run workflows on the desktop or deploy them in a server or cloud environment.

Safe Software

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

Buyer's Guide

Azure Data Factory vs. FME

September 2025

Free Report: Azure Data Factory vs. FME

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