Try our new research platform with insights from 80,000+ expert users

Azure Data Factory vs Magic xpi Integration Platform comparison

 

Comparison Buyer's Guide

Executive Summary

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
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
92
Ranking in other categories
Data Integration (1st), Cloud Data Warehouse (2nd)
Magic xpi Integration Platform
Average Rating
3.0
Number of Reviews
1
Ranking in other categories
Integration Platform as a Service (iPaaS) (28th)
 

Mindshare comparison

Azure Data Factory and Magic xpi Integration Platform aren’t in the same category and serve different purposes. Azure Data Factory is designed for Data Integration and holds a mindshare of 5.2%, down 11.0% compared to last year.
Magic xpi Integration Platform, on the other hand, focuses on Integration Platform as a Service (iPaaS), holds 0.8% mindshare, up 0.3% since last year.
Data Integration Market Share Distribution
ProductMarket Share (%)
Azure Data Factory5.2%
Informatica PowerCenter6.0%
SSIS5.7%
Other83.1%
Data Integration
Integration Platform as a Service (iPaaS) Market Share Distribution
ProductMarket Share (%)
Magic xpi Integration Platform0.8%
Boomi iPaaS12.5%
Microsoft Azure Logic Apps12.4%
Other74.3%
Integration Platform as a Service (iPaaS)
 

Featured Reviews

KandaswamyMuthukrishnan - PeerSpot reviewer
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.
it_user977634 - PeerSpot reviewer
A low-performing integration tool
We use it as an in-house back-type integration tool. It allows us to have different integrations between different systems It does not perform well. It needs more reusable components that are unlimited in time. Furthermore, it relies on the files systems and does not create components, so it is…

Quotes from Members

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

Pros

"This solution has provided us with an easier, and more efficient way to carry out data migration tasks."
"The interface of Azure Data Factory is very usable with a more interactive visual experience, making it easier for people who are not as experienced in coding to work with."
"Data Factory itself is great. It's pretty straightforward. You can easily add sources, join and lookup information, etc. The ease of use is pretty good."
"ADF is another ETL tool similar to Informatica that can transform data or copy it from on-prem to the cloud or vice versa. Once we have the data, we can apply various transformations to it and schedule our pipeline according to our business needs. ADF integrates with Databricks. We can call our Databricks notebooks and schedule them via ADF."
"It's extremely consistent."
"From my experience so far, the best feature is the ability to copy data to any environment. We have 100 connects and we can connect them to the system and copy the data from its respective system to any environment. That is the best feature."
"The initial setup is very quick and easy."
"In terms of my personal experience, it works fine."
"The stability of the solution is OK."
 

Cons

"DataStage is easier to learn than Data Factory because it's more visual. Data Factory has some drag-and-drop options, but it's not as intuitive as DataStage. It would be better if they added more drag-and-drop features. You can start using DataStage without knowing the code. You don't need to learn how the code works before using the solution."
"I would like to be informed about the changes ahead of time, so we are aware of what's coming."
"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."
"The performance could be better. It would be better if Azure Data Factory could handle a higher load. I have heard that it can get overloaded, and it can't handle it."
"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."
"If the user interface was more user friendly and there was better error feedback, it would be helpful."
"Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there."
"I do not have any notes for improvement."
"It is not performing well."
 

Pricing and Cost Advice

"Azure Data Factory gives better value for the price than other solutions such as Informatica."
"ADF is cheaper compared to AWS."
"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."
"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."
"Pricing is comparable, it's somewhere in the middle."
"The licensing is a pay-as-you-go model, where you pay for what you consume."
"It's not particularly expensive."
"The solution's pricing is competitive."
Information not available
report
Use our free recommendation engine to learn which Data Integration solutions are best for your needs.
868,787 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
12%
Manufacturing Company
9%
Government
7%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise19
Large Enterprise55
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...
Ask a question
Earn 20 points
 

Also Known As

No data available
Magic xpi Integration Platform, iBOLT
 

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
Godrej Properties
Find out what your peers are saying about Microsoft, Informatica, Talend and others in Data Integration. Updated: September 2025.
868,787 professionals have used our research since 2012.