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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) (27th)
 

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 7.4%, down 11.9% compared to last year.
Magic xpi Integration Platform, on the other hand, focuses on Integration Platform as a Service (iPaaS), holds 0.7% 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

"It's extremely consistent."
"I like its integration with SQL pools, its ability to work with Databricks, its pipelines, and the serverless architecture are the most effective features."
"The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components."
"This solution has provided us with an easier, and more efficient way to carry out data migration tasks."
"Feature-wise, one of the most valuable ones is the data flows introduced recently in the solution."
"The best part of this product is the extraction, transformation, and load."
"In terms of my personal experience, it works fine."
"Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness."
"The stability of the solution is OK."
 

Cons

"Areas for improvement in Azure Data Factory include connectivity and integration. When you use integration runtime, whenever there's a failure, the backup process in Azure Data Factory takes time, so this is another area for improvement."
"Data Factory has so many features that it can be a little difficult or confusing to find some settings and configurations. I'm sure there's a way to make it a little easier to navigate."
"Data Factory's monitorability could be better."
"When working with AWS, we have noticed that the difference between ADF and AWS is that AWS is more customer-focused. They're more responsive compared to any other company. ADF is not as good as AWS, but it should be. If AWS is ten out of ten, ADF is around eight out of ten. I think AWS is easier to understand from the GUI perspective compared to ADF."
"We have experienced some issues with the integration. This is an area that needs improvement."
"It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory."
"It would be better if it had machine learning capabilities."
"My only problem is the seamless connectivity with various other databases, for example, SAP."
"It is not performing well."
 

Pricing and Cost Advice

"Our licensing fees are approximately 15,000 ($150 USD) per month."
"It's not particularly expensive."
"The solution is cheap."
"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."
"I don't see a cost; it appears to be included in general support."
"Data Factory is expensive."
"The pricing is a bit on the higher end."
"The licensing is a pay-as-you-go model, where you pay for what you consume."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
14%
Computer Software Company
12%
Manufacturing Company
9%
Government
6%
Printing Company
14%
Computer Software Company
14%
Retailer
14%
Financial Services Firm
10%
 

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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