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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
6.8
Number of Reviews
94
Ranking in other categories
Data Integration (3rd), 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) (32nd)
 

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 2.8%, down 9.7% compared to last year.
Magic xpi Integration Platform, on the other hand, focuses on Integration Platform as a Service (iPaaS), holds 1.1% mindshare, up 0.4% since last year.
Data Integration Mindshare Distribution
ProductMindshare (%)
Azure Data Factory2.8%
SSIS3.6%
Informatica Intelligent Data Management Cloud (IDMC)3.6%
Other90.0%
Data Integration
Integration Platform as a Service (iPaaS) Mindshare Distribution
ProductMindshare (%)
Magic xpi Integration Platform1.1%
Boomi iPaaS8.0%
MuleSoft Anypoint Platform6.7%
Other84.2%
Integration Platform as a Service (iPaaS)
 

Featured Reviews

KandaswamyMuthukrishnan - PeerSpot reviewer
Director at a computer software company with 1,001-5,000 employees
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
Enterprise IT Architect at a consumer goods company with 1,001-5,000 employees
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

"The most valuable aspect is the copy capability."
"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."
"Azure Data Factory is a low code, no code platform, which is helpful."
"From what we have seen so far, the solution seems very stable."
"I like the basic features like the data-based pipelines."
"On the tool itself, we've never experienced any bugs or glitches. There haven't been crashes. Stability has been good."
"I like how you can create your own pipeline in your space and reuse those creations. You can collaborate with other people who want to use your code."
"It has built-in connectors for more than 100 sources and onboarding data from many different sources to the cloud environment."
"The stability of the solution is OK."
"The stability of the solution is OK."
 

Cons

"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."
"It does not appear to be as rich as other ETL tools. It has very limited capabilities."
"Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."
"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."
"The solution should offer better integration with Azure machine learning. We should be able to embed the cognitive services from Microsoft, for example as a web API. It should allow us to embed Azure machine learning in a more user-friendly way."
"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."
"They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas."
"If the user interface was more user friendly and there was better error feedback, it would be helpful."
"It is not performing well."
"It does not perform well. It needs more reusable components that are unlimited in time."
 

Pricing and Cost Advice

"The price is fair."
"Product is priced at the market standard."
"The solution is cheap."
"The solution's pricing is competitive."
"Pricing is comparable, it's somewhere in the middle."
"Our licensing fees are approximately 15,000 ($150 USD) per month."
"I don't see a cost; it appears to be included in general support."
"I would rate Data Factory's pricing nine out of ten."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
12%
Computer Software Company
11%
Manufacturing Company
9%
Government
6%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise20
Large Enterprise57
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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