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Azure Data Factory vs Infogix Data360 Analyze [EOL] 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
6.7
Number of Reviews
96
Ranking in other categories
Data Integration (4th), Cloud Data Warehouse (5th)
Infogix Data360 Analyze [EOL]
Average Rating
7.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

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.
reviewer1321299 - PeerSpot reviewer
Data Analytics Consultant at velocity
Easy drag-and-drop interface and supports custom Python functions, but the performance needs to be better
The memory processing needs to be improved because when you deal with a large amount of data, the interface tends to hang a little bit. When the system boots up, it can take between two and five minutes, depending on the system memory (RAM). If the system is low on memory then it takes a long time to start up. If you are not familiar with Python then this product will be a little more difficult for you. It can take a long time to migrate from one version to the next because there are a lot of processes to deal with.

Quotes from Members

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

Pros

"The function of the solution is great."
"We haven't had any issues connecting it to other products."
"In terms of my personal experience, it works fine."
"Azure Data Factory is great because it's a cloud service; you do not have to take care of the installation and configuration yourself."
"The platform excels in data transformation with its user-friendly interface and robust monitoring capabilities, making ETL processes seamless."
"We use the solution to move data from on-premises to the cloud."
"Azure Data Factory is a very easy to use ETL tool for loading and transforming data from one location to another."
"For developers that are very accustomed to the Microsoft development studio, it's very easy for them to complete end-to-end data integration."
"This solution allows you to include your own functions that are coded in Python."
"The drag-and-drop functionality makes it easy for business users."
 

Cons

"It's essentially just a black box. There is some monitoring that can be done, but when something goes wrong, even simple fixes are difficult to troubleshoot."
"Data Factory's monitorability could be better."
"Occasionally, there are problems within Microsoft itself that impacts the Data Factory and causes it to fail."
"We require Azure Data Factory to be able to connect to Google Analytics."
"Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."
"I would like to be informed about the changes ahead of time, so we are aware of what's coming."
"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."
"The performance and stability are touch and go."
"The memory processing needs to be improved because when you deal with a large amount of data, the interface tends to hang a little bit."
 

Pricing and Cost Advice

"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."
"The licensing is a pay-as-you-go model, where you pay for what you consume."
"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."
"It's not particularly expensive."
"I would not say that this product is overly expensive."
"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."
"Azure Data Factory gives better value for the price than other solutions such as Informatica."
"Product is priced at the market standard."
"The open-source version is free to use, although it has a limitation of two-million records."
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Top Industries

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

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise21
Large Enterprise63
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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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
citi, swedbank, RSA, MasterCard, travelers, telstra
Find out what your peers are saying about Informatica, Microsoft, Palantir and others in Data Integration. Updated: June 2026.
902,456 professionals have used our research since 2012.