Azure Data Factory vs IBM Cloud Pak for Data comparison

Microsoft and IBM are both solutions in the Data Integration category. Microsoft is ranked #1 with an average rating of 8.2, while IBM is ranked #25 with an average rating of 7.0. Microsoft holds a 5.6% mindshare in DI, compared to IBM’s 1.8% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 86% of IBM users who would recommend it.

Azure Data Factory

Read 92 Azure Data Factory reviews

14,334 Views
10,034 Comparison Views

92% willing to recommend

IBM Cloud Pak for Data

Read 13 IBM Cloud Pak for Data reviews

2,309 Views
2,170 Comparison Views

86% willing to recommend

Azure Data Factory

IBM Cloud Pak for Data

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 19, 2024

Azure Data Factory and IBM Cloud Pak for Data are competing in the data integration and analytics category. Azure Data Factory has an edge due to its extensive integration features and more flexible pricing model, making it more accessible to a broader range of users.

Features: Azure Data Factory offers flexibility and integrability with over 100 built-in connectors, simplifying data integration. Key features like data flow assist in creating ETL pipelines, and Databricks integration enhances data transformation. It also boasts a drag-and-drop feature, facilitating intuitive orchestration. IBM Cloud Pak for Data offers Watson Studio and Machine Learning capabilities for data governance and AI model development. The combination of AI tools presents comprehensive data handling and analysis capabilities, enhancing its value.

Room for Improvement: Azure Data Factory could improve its integration with existing Azure services, streamline user-friendliness, and simplify its pricing model. Users request more out-of-the-box connectors, better real-time processing features, and comprehensive documentation. IBM Cloud Pak for Data needs a simplified setup process, more connectors, and enhanced performance stability. Better integration with other cloud providers and improved data curation features are also areas for development.

Ease of Deployment and Customer Service: Azure Data Factory excels in public cloud environments providing a scalable solution but often requires external expertise. Support is generally responsive but with mixed community feedback. IBM Cloud Pak for Data supports public and hybrid cloud deployments but faces challenges in starting small due to infrastructure needs. Simplified customer service could improve user experience as the setup can be complex.

Pricing and ROI: Azure Data Factory's pay-as-you-go model aligns costs with consumption, making it affordable but sometimes unpredictable. It provides value through workflow efficiency, though advanced services might increase costs. IBM Cloud Pak for Data requires substantial investment, justified by its capabilities, primarily benefiting larger enterprises. Its high cost can limit accessibility for smaller businesses compared to Azure's flexible pricing.

To learn more, read our detailed Azure Data Factory vs. IBM Cloud Pak for Data Report (Updated: September 2025).

Buyer's Guide

Azure Data Factory vs. IBM Cloud Pak for Data

September 2025

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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

Ranking in other categories

Cloud Data Warehouse (2nd)

IBM Cloud Pak for Data

Ranking in Data Integration

25th

Average Rating

7.8

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

Data Virtualization (3rd)

Mindshare comparison

As of September 2025, in the Data Integration category, the mindshare of Azure Data Factory is 5.6%, down from 11.6% compared to the previous year. The mindshare of IBM Cloud Pak for Data is 1.8%, up from 1.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Market Share Distribution
Product	Market Share (%)
Azure Data Factory	5.6%
IBM Cloud Pak for Data	1.8%
Other	92.6%

Data Integration

Featured Reviews

KandaswamyMuthukrishnan

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.

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Michelle Leslie

Data asset management engineer at a tech services company with 1-10 employees

Starts strong with data management capabilities but needs a demo database

What I would love to see is an end-to-end, almost a training demo database of some sort, where one of the biggest problems with data management is demonstrated. There are so many components to data management, and more often than not, people understand one thing really well. They may understand DataStage and how to move data around, but they do not see the impact of moving data incorrectly. They also do not see the impact of everyone understanding a piece of data in the same way. I would love Cloud Pak to come with a demo database that illustrates the different components of data management in a logical way, so I can see the whole picture instead of just the area I'm specializing in. It would be great if Cloud Pak, from a data modeling point of view, allowed us to import our PDMs, for example. It would be ideal to import and create business terms in Cloud Pak. The PEA would be great to create the technical data. The association between the business and the technical metadata could then be automated by pulling it through from your ACE models. The data modeling component is available in Cloud Pak. Additionally, when it comes to Cloud Pak, even though it has the NextGen DataStage built into it, there is Cloud Pak for data integration as well. Currently, I do not think we have a full enough understanding of how CP4D and CP4I can enhance each other.

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Quotes from Members

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

Pros

"The solution has a good interface and the integration with GitHub is very useful."

"In terms of my personal experience, it works fine."

"The platform excels in data transformation with its user-friendly interface and robust monitoring capabilities, making ETL processes seamless."

"When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit."

"The initial setup is very quick and easy."

"Powerful but easy-to-use and intuitive."

"The most valuable feature I have found at Azure Data Factory is the data flow function."

"The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted."

More Azure Data Factory pros

"One of Cloud Pak's best features is the Watson Knowledge Catalog, which helps you implement data governance."

"IBM Watson Catalog and data pipelines are the most valuable features of the solution."

"Cloud Pak's most valuable features are IBM MQ, IBM App Connect, IBM API Connect, and ISPF."

"The most valuable features are data virtualization and reporting."

"DataStage allows me to connect to different data sources."

"It is a scalable solution, and we have had no issues with its scalability in our company. I rate the solution's scalability a nine out of ten."

"I love the way that I can start at a very basic level with my data management journey by capturing my policies, justifying my data, and putting them into different categories to say this is data relating to individuals, for example, or data relating to geography."

"The most valuable features of IBM Cloud Pak for Data are the Watson Studio, where we can initiate more groups and write code. Additionally, Watson Machine Learning is available with many other services, such as APIs which you can plug the machine learning models."

More IBM Cloud Pak for Data pros

Cons

"The speed and performance need to be improved."

"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."

"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."

"The product's technical support has certain shortcomings, making it an area where improvements are required."

"Azure Data Factory could benefit from improvements in its monitoring capabilities to provide a more robust feature set. Enhancing the ease of deployment to higher environments within Azure DevOps would be beneficial, as the current process often requires extensive scripting and pipeline development. It is also known for the flexibility of the data flow feature, particularly in supporting more dynamic data-driven architectures. These enhancements would contribute to a more seamless and efficient workflow within GitLab."

"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."

"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."

"Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically."

More Azure Data Factory cons

"The tool depends on the control plane, an OpenShift container platform utilized as an orchestration layer...So, we have communicated this issue to IBM and asked if it is feasible to adapt the solution to work on a Kubernetes platform that we support."

"The solution's user experience is an area that has room for improvement."

"What I would love to see is an end-to-end, almost a training demo database of some sort, where one of the biggest problems with data management is demonstrated."

"One challenge I'm facing with IBM Cloud Pak for Data is native features have been decommissioned, such as XML input and output. Too many changes have been made, and my company has around one hundred thousand mappings, so my team has been putting more effort into alternative ways to do things. Another area for improvement in IBM Cloud Pak for Data is that it's more complicated to shift from on-premise to the cloud. Other vendors provide secure agents that easily connect with your existing setup. Still, with IBM Cloud Pak for Data, you have to perform connection migration steps, upgrade to the latest version, etc., which makes it more complicated, especially as my company has XML-based mappings. Still, the XML input and output capabilities of IBM Cloud Pak for Data have been discontinued, so I'd like IBM to bring that back."

"The product must improve its performance."

"The solution's catalog searching or map search needs to be improved."

"There is a solution that is part of IBM Cloud Pak for Data called Watson OpenScale. It is used to monitor the deployed models for the quality and fairness of the results. This is one area that needs a lot of improvement."

"The product is trying to be more maturity in terms of connectors. That, I believe, is an area where Cloud Pak can improve."

More IBM Cloud Pak for Data cons

Pricing and Cost Advice

"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."

"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."

"In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal."

"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."

"I don't see a cost; it appears to be included in general support."

"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."

More Azure Data Factory pricing and cost advice

"The solution's pricing is competitive with that of other vendors."

"I think that this product is too expensive for smaller companies."

"I don't have the exact licensing cost for IBM Cloud Pak for Data, as my company is still finalizing requirements, including monthly, yearly, and three-year licensing fees. Still, on a scale of one to five, I'd rate it a three because, compared to other vendors, it's more complicated."

"The solution is expensive."

"Cloud Pak's cost is a little high."

"It's quite expensive."

"IBM Cloud Pak for Data is expensive. If we include the training time and the machine learning, it's expensive. The cost of the execution is more reasonable."

"For the licensing of the solution, there is a yearly payment that needs to be made. Also, since it is expensive, cost-wise, I rate the solution an eight or nine out of ten."

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Top Industries

By visitors reading reviews

Financial Services Firm

13%

Computer Software Company

12%

Manufacturing Company

Government

Financial Services Firm

30%

Manufacturing Company

10%

Computer Software Company

Government

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	19
Large Enterprise	55

By reviewers
Company Size	Count
Small Business	7
Large Enterprise	8

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.

See all answers

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...

See all answers

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...

See all answers

What is your experience regarding pricing and costs for IBM Cloud Pak for Data?

The setup cost is very expensive. The cost depends on the pieces of the solution I'm using, how much data I have, and whether it's on the cloud or on-prem.

See all answers

What needs improvement with IBM Cloud Pak for Data?

See all answers

What is your primary use case for IBM Cloud Pak for Data?

My primary use case for Cloud Pak is that I am the reference Data steward for the Africa regions in the banks where I work. My main objective is to capture the reference data in Caltech or Data and...

See all answers

Comparisons

Snowflake vs Azure Data Factory

Compared 8% of the time

Informatica PowerCenter vs Azure Data Factory

Compared 6% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Azure Data Factory

Compared 6% of the time

Palantir Foundry vs Azure Data Factory

Compared 5% of the time

FME vs Azure Data Factory

Compared 5% of the time

More Azure Data Factory Competitors

IBM InfoSphere DataStage vs IBM Cloud Pak for Data

Compared 44% of the time

Denodo vs IBM Cloud Pak for Data

Compared 13% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs IBM Cloud Pak for Data

Compared 11% of the time

Palantir Foundry vs IBM Cloud Pak for Data

Compared 9% of the time

Informatica PowerCenter vs IBM Cloud Pak for Data

Compared 4% of the time

More IBM Cloud Pak for Data Competitors

Product Reports

Buyer's Guide

Azure Data Factory

September 2025

Download Azure Data Factory product report

Buyer's Guide

IBM Cloud Pak for Data

September 2025

Download IBM Cloud Pak for Data product report

Also Known As

No data available

Cloud Pak for Data

Overview

Azure Data Factory efficiently manages and integrates data from various sources, enabling seamless movement and transformation across platforms. Its valuable features include seamless integration with Azure services, handling large data volumes, flexible transformation, user-friendly interface, extensive connectors, and scalability. Users have experienced improved team performance, workflow simplification, enhanced collaboration, streamlined processes, and boosted productivity.

Microsoft

IBM Cloud Pak® for Data is a fully-integrated data and AI platform that modernizes how businesses collect, organize and analyze data to infuse AI throughout their organizations. Cloud-native by design, the platform unifies market-leading services spanning the entire analytics lifecycle. From data management, DataOps, governance, business analytics and automated AI, IBM Cloud Pak for Data helps eliminate the need for costly, and often competing, point solutions while providing the information architecture you need to implement AI successfully.

Building on the streamlined hybrid-cloud foundation of Red Hat® OpenShift®, IBM Cloud Pak for Data takes advantage of the underlying resource and infrastructure optimization and management. The solution fully supports multicloud environments such as Amazon Web Services (AWS), Azure, Google Cloud, IBM Cloud™ and private cloud deployments. Find out how IBM Cloud Pak for Data can lower your total cost of ownership and accelerate innovation.

IBM

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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Buyer's Guide

Azure Data Factory vs. IBM Cloud Pak for Data

September 2025

Free Report: Azure Data Factory vs. IBM Cloud Pak for Data

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