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Azure Data Factory vs IBM Db2 Warehouse on Cloud comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 18, 2024

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 Cloud Data Warehouse
2nd
Average Rating
8.0
Reviews Sentiment
6.8
Number of Reviews
93
Ranking in other categories
Data Integration (3rd)
IBM Db2 Warehouse on Cloud
Ranking in Cloud Data Warehouse
15th
Average Rating
7.6
Reviews Sentiment
6.3
Number of Reviews
7
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of December 2025, in the Cloud Data Warehouse category, the mindshare of Azure Data Factory is 6.1%, down from 9.2% compared to the previous year. The mindshare of IBM Db2 Warehouse on Cloud is 1.1%, up from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse Market Share Distribution
ProductMarket Share (%)
Azure Data Factory6.1%
IBM Db2 Warehouse on Cloud1.1%
Other92.8%
Cloud Data Warehouse
 

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.
FM
Database Engineer at Meezan Bank
Enhancing analytics with seamless data dumping and reliable support
Our primary use case is data storage and analytics The organization has decided to purchase a full stack solution from IBM due to positive responses, which helped them upgrade from the previous version. The data dumping into the raw zone and the feature of BigQuery is quite attractive. There…

Quotes from Members

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

Pros

"Data Factory itself is great. It's pretty straightforward. You can easily add sources, join and lookup information, etc. The ease of use is pretty good."
"The security of the agent that is installed on-premises is very good."
"The most valuable feature of this solution would be ease of use."
"Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations."
"I find that the solution integrates well with cloud technologies, which we are using for different clouds like Snowflake and AWS"
"I think it makes it very easy to understand what data flow is and so on. You can leverage the user interface to do the different data flows, and it's great. I like it a lot."
"It is easy to deploy workflows and schedule jobs."
"Data Factory's best features include its data source connections, GUI for building data pipelines, and target loading within Azure."
"The performance is okay as long as the volume of queries is not too high."
"It is stable when there is support from IBM."
"It will be MPP, so performance should improve."
"The way that it scales will help a lot of customers that are stuck with Netezza boxes that can't grow any larger.​"
 

Cons

"Snowflake connectivity was recently added and if the vendor provided some videos on how to create data then that would be helpful."
"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."
"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog."
"Some of the optimization techniques are not scalable."
"The pricing scheme is very complex and difficult to understand."
"The one element of the solution that we have used and could be improved is the user interface."
"Azure Data Factory uses many resources and has issues with parallel workflows."
"On the UI side, they could make it a little more intuitive in terms of how to add the radius components. Somebody who has been working with tools like Informatica or DataStage gets very used to how the UI looks and feels."
"Ultimately, the product itself has challenges and we are not currently satisfied with the support, either."
"Containers get corrupted very easily. Restoring them using GPFS can result in a lot of issues."
"Right now, we are implementing on ESX VMware 6.0. Support for this platform is poor. Also, one of the backup/recovery options is broken and IBM is not addressing the issue."
"Tech support for dashDB is awful. We usually have tickets open for three to four weeks."
"There are some limitations in adding data files to table spaces, and improvements are needed for regional support."
 

Pricing and Cost Advice

"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."
"The cost is based on the amount of data sets that we are ingesting."
"The pricing is a bit on the higher end."
"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."
"The price you pay is determined by how much you use it."
"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."
"The licensing is a pay-as-you-go model, where you pay for what you consume."
"Our licensing fees are approximately 15,000 ($150 USD) per month."
"If your going to go with warehouse DB/dashDB, use the cloud or Sailfish version."
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
12%
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
By reviewers
Company SizeCount
Small Business4
Large Enterprise3
 

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...
What advice do you have for others considering IBM Db2 Warehouse on Cloud?
Organizations of all sizes, especially those who are in need of powerful and elastic cloud data warehouse solutions that can help administrators maximize the efficiency of their data-based operatio...
What needs improvement with IBM Db2 Warehouse on Cloud?
There are some limitations in adding data files to table spaces, and improvements are needed for regional support.
What is your primary use case for IBM Db2 Warehouse on Cloud?
Our primary use case is data storage and analytics.
 

Also Known As

No data available
IBM dashDB
 

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
Copenhagen Business School, BPM Northwest, GameStop
Find out what your peers are saying about Azure Data Factory vs. IBM Db2 Warehouse on Cloud and other solutions. Updated: December 2025.
879,371 professionals have used our research since 2012.