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 8.0, while Safe Software is ranked #26 with an average rating of 8.7. Microsoft holds a 11.1% mindshare in DI, compared to Safe Software’s 2.0% 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 86 Azure Data Factory reviews

23,087 Views
18,331 Comparison Views

92% willing to recommend

FME

Read 5 FME reviews

2,886 Views
2,157 Comparison Views

100% willing to recommend

Azure Data Factory

FME

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Azure Data Factory and FME based on real PeerSpot user reviews.

Find out in this report how the two Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Azure Data Factory vs. FME Report (Updated: October 2024).

Buyer's Guide

Azure Data Factory vs. FME

October 2024

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Categories and Ranking

Azure Data Factory

Ranking in Data Integration

1st

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (3rd)

FME

Ranking in Data Integration

26th

Average Rating

8.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2024, in the Data Integration category, the mindshare of Azure Data Factory is 11.1%, down from 13.3% compared to the previous year. The mindshare of FME is 2.0%, up from 1.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration

Featured Reviews

Thulani David Mngadi

Data architect at Old Mutual

Data flow feature is valuable for data transformation tasks

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.

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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

"From what we have seen so far, the solution seems very stable."

"The most valuable feature of this solution would be ease of use."

"We have found the bulk load feature very valuable."

"The valuable feature of Azure Data Factory is its integration capability, as it goes well with other components of Microsoft Azure."

"The platform excels in data transformation with its user-friendly interface and robust monitoring capabilities, making ETL processes seamless."

"The initial setup is very quick and easy."

"An excellent tool for pipeline orchestration."

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

More Azure Data Factory pros

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

"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."

"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 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."

Cons

"We require Azure Data Factory to be able to connect to Google Analytics."

"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."

"I rate Azure Data Factory six out of 10 for stability. ADF is stable now, but we had problems recently with indexing on an SQL database. It's slow when dealing with a huge volume of data. It depends on whether the database is configured as general purpose or hyperscale."

"When we initiated the cluster, it took some time to start the process."

"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."

"The number of standard adaptors could be extended further."

"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."

"I would like to see this time travel feature in Snowflake added to Azure Data Factory."

More Azure Data Factory cons

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

"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."

"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."

"Improvements could be made to mapping presentations."

"FME's price needs improvement for the African market."

Pricing and Cost Advice

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

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

"This is a cost-effective solution."

"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 price is fair."

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

"The solution is cheap."

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

More Azure Data Factory pricing and cost advice

"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."

"FME Server used to cost £10,000; now it can cost over £100,000."

"The product's price is reasonable."

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816,406 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

13%

Computer Software Company

12%

Manufacturing Company

Healthcare Company

Government

29%

Energy/Utilities Company

12%

Computer Software Company

Manufacturing Company

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.

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?

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...

See all answers

Comparisons

Informatica PowerCenter vs Azure Data Factory

Compared 14% of the time

Snowflake vs Azure Data Factory

Compared 11% of the time

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

Compared 8% of the time

Alteryx Designer vs Azure Data Factory

Compared 7% of the time

Microsoft Azure Synapse Analytics vs Azure Data Factory

Compared 2% of the time

More Azure Data Factory Competitors

Talend Open Studio vs FME

Compared 16% of the time

Alteryx Designer vs FME

Compared 15% of the time

SSIS vs FME

Compared 9% of the time

Informatica PowerCenter vs FME

Compared 6% of the time

AWS Glue vs FME

Compared 5% of the time

More FME Competitors

Product Reports

Buyer's Guide

Azure Data Factory

November 2024

Download Azure Data Factory product report

Buyer's Guide

FME

November 2024

Download FME product report

Learn More

Microsoft

Safe Software

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.

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.

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

October 2024

Free Report: Azure Data Factory vs. FME

Find out what your peers are saying about Azure Data Factory vs. FME and other solutions. Updated: October 2024.

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