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Azure Data Factory vs Infogix Data360 Analyze [EOL] comparison

 

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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
6.9
Number of Reviews
90
Ranking in other categories
Data Integration (1st), Cloud Data Warehouse (3rd)
Infogix Data360 Analyze [EOL]
Average Rating
7.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

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.
reviewer1321299 - PeerSpot reviewer
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

"Azure Data Factory became more user-friendly when data-flows were introduced."
"The solution includes a feature that increases the number of processors used which makes it very powerful and adds to the scalability."
"The interface of Azure Data Factory is very usable with a more interactive visual experience, making it easier for people who are not as experienced in coding to work with."
"The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain."
"For me, it was that there are dedicated connectors for different targets or sources, different data sources. For example, there is direct connector to Salesforce, Oracle Service Cloud, etcetera, and that was really helpful."
"It is beneficial that the solution is written with Spark as the back end."
"I find the most valuable feature in Azure Data Factory to be its ability to handle large datasets."
"Its integrability with the rest of the activities on Azure is most valuable."
"The drag-and-drop functionality makes it easy for business users."
 

Cons

"The main challenge with implementing Azure Data Factory is that it processes data in batches, not near real-time. To achieve near real-time processing, we need to schedule updates more frequently, which can be an issue. Its interface needs to be lighter."
"There is a problem with the integration with third-party solutions, particularly with SAP."
"Data Factory's monitorability could be better."
"There's no Oracle connector if you want to do transformation using data flow activity, so Azure Data Factory needs more connectors for data flow transformation."
"For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better."
"When the record fails, it's tough to identify and log."
"Some prebuilt data source or data connection aspects are generic."
"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."
"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 rate the product price as six on a scale of one to ten, where one is low price and ten is high price."
"The price is fair."
"The solution is cheap."
"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."
"The solution's pricing is competitive."
"Understanding the pricing model for Data Factory is quite complex."
"I would rate Data Factory's pricing nine out of ten."
"The pricing is a bit on the higher end."
"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
14%
Computer Software Company
12%
Manufacturing Company
9%
Healthcare Company
7%
Financial Services Firm
40%
Healthcare Company
16%
Insurance Company
7%
University
7%
 

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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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
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