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Azure Data Factory vs DBamp 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
Ranking in Data Integration
1st
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
90
Ranking in other categories
Cloud Data Warehouse (3rd)
DBamp
Ranking in Data Integration
67th
Average Rating
8.0
Reviews Sentiment
8.7
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of February 2025, in the Data Integration category, the mindshare of Azure Data Factory is 10.1%, down from 12.9% compared to the previous year. The mindshare of DBamp is 0.1%, down from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration
 

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.
reviewer2542599 - PeerSpot reviewer
Integration with existing tools enhances data handling capabilities
DBM allows integration with SQL Server, which is beneficial for Microsoft. While we are only downloading at the moment, we have tested some uploads, and the performance seems better than Data Loader, though it is more complicated. Both products use recommended security protocols, and we don't worry about their security.

Quotes from Members

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

Pros

"This solution has provided us with an easier, and more efficient way to carry out data migration tasks."
"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 flexibility that Azure Data Factory offers is great."
"The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain."
"One advantage of Azure Data Factory is that it's fast, unlike SSIS and other on-premise tools. It's also very convenient because it has multiple connectors. The availability of native connectors allows you to connect to several resources to analyze data streams."
"The most valuable feature is the copy activity."
"Azure Data Factory is a low code, no code platform, which is helpful."
"The overall performance is quite good."
"Their support is good, very responsive despite being overseas."
"DBM allows integration with SQL Server, which is beneficial for Microsoft."
 

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."
"The support and the documentation can be improved."
"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."
"When the record fails, it's tough to identify and log."
"There's space for improvement in the development process of the data pipelines."
"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."
"DataStage is easier to learn than Data Factory because it's more visual. Data Factory has some drag-and-drop options, but it's not as intuitive as DataStage. It would be better if they added more drag-and-drop features. You can start using DataStage without knowing the code. You don't need to learn how the code works before using the solution."
"We have experienced some issues with the integration. This is an area that needs improvement."
"DBM is a bit more complicated when compared to DataLoader, especially for more complex operations."
"DBM is a bit more complicated when compared to DataLoader, especially for more complex operations."
 

Pricing and Cost Advice

"I would not say that this product is overly expensive."
"Pricing appears to be reasonable in my opinion."
"The licensing cost is included in the Synapse."
"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."
"Our licensing fees are approximately 15,000 ($150 USD) per month."
"There's no licensing for Azure Data Factory, they have a consumption payment model. How often you are running the service and how long that service takes to run. The price can be approximately $500 to $1,000 per month but depends on the scaling."
"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."
"Understanding the pricing model for Data Factory is quite complex."
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
12%
Manufacturing Company
9%
Healthcare Company
7%
No data available
 

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
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