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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
7.0
Number of Reviews
91
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) (24th)
 

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 8.4%, down 12.3% compared to last year.
Magic xpi Integration Platform, on the other hand, focuses on Integration Platform as a Service (iPaaS), holds 0.5% mindshare, up 0.3% since last year.
Data Integration
Integration Platform as a Service (iPaaS)
 

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.
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

"I can do everything I want with SSIS and Azure Data Factory."
"From what we have seen so far, the solution seems very stable."
"The most important feature is that it can help you do the multi-threading concepts."
"UI is easy to navigate and I can retrieve VTL code without knowing in-depth coding languages."
"We haven't had any issues connecting it to other products."
"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."
"We use the solution to move data from on-premises to the cloud."
"The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain."
"The stability of the solution is OK."
 

Cons

"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."
"Azure Data Factory uses many resources and has issues with parallel workflows."
"The speed and performance need to be improved."
"We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on."
"User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial."
"In the next release, it's important that some sort of scheduler for running tasks is added."
"Currently, our company requires a monitoring tool, and that isn't available in Azure Data Factory."
"The solution can be improved by decreasing the warmup time which currently can take up to five minutes."
"It is not performing well."
 

Pricing and Cost Advice

"I would not say that this product is overly expensive."
"The cost is based on the amount of data sets that we are ingesting."
"The solution is cheap."
"The solution's pricing is competitive."
"The price you pay is determined by how much you use it."
"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."
"My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use. On a scale of one to five, pricing for Azure Data Factory is a four. It's just the usage fees my company pays monthly."
"Data Factory is affordable."
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Top Industries

By visitors reading reviews
Financial Services Firm
14%
Computer Software Company
12%
Manufacturing Company
9%
Healthcare Company
6%
Retailer
14%
Printing Company
14%
Computer Software Company
12%
Performing Arts
8%
 

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...
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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
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