Azure Data Factory vs FME comparison

Why PeerSpot?

Microsoft and Safe Software are both solutions in the Data Integration category. Microsoft is ranked #5 with an average rating of 8.0, while Safe Software is ranked #31 with an average rating of 8.0. Microsoft holds a 2.2% mindshare in DI, compared to Safe Software’s 0.9% 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 97 Azure Data Factory reviews

14,793 Views
10,207 Comparison Views

92% willing to recommend

FME

Read 7 FME reviews

3,790 Views
2,918 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 2026).

Buyer's Guide

Azure Data Factory vs. FME

September 2026

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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

5th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (7th)

FME

Ranking in Data Integration

31st

Average Rating

8.6

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of September 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.2%, down from 5.5% compared to the previous year. The mindshare of FME is 0.9%, down from 1.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Mindshare Distribution
Product	Mindshare (%)
Azure Data Factory	2.2%
FME	0.9%
Other	96.9%

Data Integration

Featured Reviews

Kunal Das

Test Engineer at Happiest Minds Technologies

Drag-and-drop pipelines have saved days of work and now automate data movement and backfilling

If the AI features were more improved so that I don't have to provide each and every detail, Azure Data Factory could be improved in a much better way by improving the AI features. For example, if I want to fetch any data from a raw source, I need to provide each and every detail. But if I am just uploading my raw data and if AI will sync with that data, it can analyze that data and give me proper suggestions on how that should be done in a proper way. Automatic suggestions could improve in a much better way. As I have mentioned, the AI features as well as more drag-and-drop activities could be improved. If I am making a pipeline, it should give me suggestions, such as which activity should be used, so that I don't have to remember each activity. If I have used one activity, I shouldn't have to remember what activity should I use next. It should give auto-suggestions. That is why I have given a nine out of 10. Currently, I don't know about its governance and security, but in view of its improvement, I think Azure Data Factory should improve in these areas. As I already mentioned, the AI features should be improved. Also, the auto-suggestion features should also improve.

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Gert Booysen

Software Solutions Leader at GE Vernova

Extensive format support and reliable integration enhance data management while new pricing model requires reassessment

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 model changes that they've implemented a while back, it actually made it more expensive to use, especially on the server side. They changed the licensing model, and that made customers think it is overpriced. Some customers were actually looking at alternatives. Pricing with the model changes was perceived negatively. I work primarily with enterprise customers, Vodacom, Eskom, so it's tier-one customers. For small customers, this solution is a bit too expensive. They don't really use it and just do direct integration on smaller implementations. This is basically used by tier-one customers. FME is still able to save time and money for clients. It's still a good investment. Regarding similar products to FME, GE developed a product called Data Fabric. That is a real-time operational integration platform, Data Fabric Network Connect, which is a GE Grid OS product. It is actually more expensive than FME, but the purpose is different as it's operational. The vendor in this case is GE, and it's a company that we bought that used to be called Greenbird. In current use scenarios, FME is still leading compared to Data Fabric. The GE product has a different application.

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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

"We have been using drivers to connect to various data sets and consume data."

"The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."

"Azure Data Factory became more user-friendly when data-flows were introduced."

"Feature-wise, one of the most valuable ones is the data flows introduced recently in the solution."

"It makes it easy to collect data from different sources."

"We use the solution to move data from on-premises to the cloud."

"Azure Data Factory is a low code, no code platform, which is helpful."

"The most valuable aspect is the copy capability."

More Azure Data Factory pros

"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."

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

"FME's features that I have found most valuable are that it has a very friendly user interface, you don't need to use a lot of code, it has a lot of connectors and few forms, and it has a strong facial aspect that can do a lot of facial analysis."

"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."

"The most valuable feature of FME is the graphical user interface."

"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."

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

Cons

"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."

"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."

"The product's technical support has certain shortcomings, making it an area where improvements are required."

"Data Factory could be improved by eliminating the need for a physical data area. We have to extract data using Data Factory, then create a staging database for it with Azure SQL, which is very, very expensive. Another improvement would be lowering the licensing cost."

"One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases."

"They should work on optimizing their licensing model and pricing structure."

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

"Azure Data Factory's pricing in terms of utilization could be improved."

More Azure Data Factory cons

"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."

"Regarding pricing, with the model changes that they've implemented a while back, it actually made it more expensive to use, especially on the server side."

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

"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."

"FME is a great tool, but I don't know if we can scale it up since the prices are high and we would need to convince the decision makers to provide more budget."

"To get a higher rating, it would have to improve the price and the associated scalability. These are the main issues."

Pricing and Cost Advice

"Understanding the pricing model for Data Factory is quite complex."

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

"Data Factory is affordable."

"I would rate Data Factory's pricing nine out of ten."

"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."

"The cost is based on the amount of data sets that we are ingesting."

"This is a cost-effective solution."

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

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

11%

Manufacturing Company

Computer Software Company

Construction Company

Government

25%

Energy/Utilities Company

11%

Comms Service Provider

Construction Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	21
Large Enterprise	64

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...

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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...

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What needs improvement with FME?

See all answers

What is your primary use case for FME?

The use cases for FME are mainly integration to the GE Smallworld product. GE Smallworld is a GE product, and we are using it to integrate with other third-party products into GE Smallworld and ADM...

See all answers

What advice do you have for others considering FME?

The overall rating for FME is eight out of ten, and I prefer the feedback to be anonymous. My job title is Senior Solutions Architect.

See all answers

Comparisons

Snowflake vs Azure Data Factory

Compared 4% of the time

Databricks vs Azure Data Factory

Compared 4% of the time

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

Compared 3% of the time

Palantir Foundry vs Azure Data Factory

Compared 3% of the time

Talend Data Fabric vs Azure Data Factory

Compared 2% of the time

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SSIS vs FME

Compared 8% of the time

Ikon vs FME

Compared 5% of the time

iWay Big Data Integrator vs FME

Compared 4% of the time

Alteryx Designer vs FME

Compared 4% of the time

Qlik Talend Cloud vs FME

Compared 4% of the time

More FME Competitors

Product Reports

Buyer's Guide

Azure Data Factory

September 2026

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Buyer's Guide

FME

September 2026

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 a powerful tool designed for efficient geospatial data handling, supporting over 400 file formats and integrating with diverse systems. Its intuitive interface facilitates seamless automation of workflows, making it ideal for sectors that manage large spatial data volumes.

With FME, businesses automate data flows and manage GIS data transfers while integrating with tools like GE Smallworld. It empowers users with self-service capabilities for data processing and real-time event management. Despite its strengths, users note areas for improvement such as better mapping presentations, simpler loops, and more guidance on geographical transformations. Pricing remains a consideration for some markets. However, FME is a robust solution for massive data transformations, network infrastructure management, and automation of data quality control, especially in governmental contexts.

What are FME's key features?

Spatial Capabilities: Efficient handling of geospatial data through triggers and automation.
Integration: Seamlessly works with FME Flow, FME Form, and scripting languages like Python.
User-Friendly Interface: Simplifies data transformation and debugging without extensive coding.
Format Support: Over 400 file formats ensure wide-ranging compatibility.
System Integration: Connects with numerous systems, preferred for large spatial data volumes.

What benefits should users consider?

Automation: Streamlines workflows and reduces manual intervention.
Efficiency: Handles large datasets effectively, optimizing data operations.
Cost-Effectiveness: Despite pricing concerns, offers significant functionality for data processing.
Self-Service Capabilities: Enables users to manage and transform data independently.
Real-Time Processing: Supports real-time event handling for dynamic environments.

FME is widely adopted across industries that require precise geospatial data management such as government agencies needing to automate and control data processes efficiently or organizations handling complex network infrastructures that engage in frequent GIS data transfers. Its capacity for seamless integration with various systems enables practical use for extensive data transformations and automation, increasing operational efficiency in data-intensive sectors.

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 2026

Free Report: Azure Data Factory vs. FME

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