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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
93
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) (30th)
 

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 3.2%, down 10.0% 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 Market Share Distribution
ProductMarket Share (%)
Azure Data Factory3.2%
SSIS4.0%
Informatica Intelligent Data Management Cloud (IDMC)3.7%
Other89.1%
Data Integration
Integration Platform as a Service (iPaaS) Market Share Distribution
ProductMarket Share (%)
Magic xpi Integration Platform1.1%
Boomi iPaaS8.6%
Microsoft Azure Logic Apps7.2%
Other83.1%
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 platform excels in data transformation with its user-friendly interface and robust monitoring capabilities, making ETL processes seamless."
"The data flows were beneficial, allowing us to perform multiple transformations."
"I find that the solution integrates well with cloud technologies, which we are using for different clouds like Snowflake and AWS"
"The most valuable feature of this solution would be ease of use."
"The solution is okay."
"The most valuable feature is the copy activity."
"I am one hundred percent happy with the stability."
"The trigger scheduling options are decently robust."
"The stability of the solution is OK."
 

Cons

"I do not have any notes for improvement."
"The setup and configuration process could be simplified."
"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."
"The product's technical support has certain shortcomings, making it an area where improvements are required."
"Some of the optimization techniques are not scalable."
"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."
"There are performance issues, particularly with the underlying compute, which should be configurable."
"It is not performing well."
 

Pricing and Cost Advice

"The cost is based on the amount of data sets that we are ingesting."
"The pricing model is based on usage and is not cheap."
"Our licensing fees are approximately 15,000 ($150 USD) per month."
"The licensing model for Azure Data Factory is good because you won't have to overpay. Pricing-wise, the solution is a five out of ten. It was not expensive, and it was not cheap."
"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."
"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."
"It's not particularly expensive."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
11%
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 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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