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Azure Data Factory vs IBM Cloud Pak for Data comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 19, 2024

Review summaries and opinions

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

ROI

Sentiment score
6.4
Azure Data Factory offers significant ROI, efficiency, and cost savings, with users highlighting benefits in data integration and migration.
Sentiment score
6.5
IBM Cloud Pak for Data helps large enterprises improve data quality, boosting AI processes and enhancing decision-making productivity.
Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness.
Data Engineer at Vthinktechnologies
We have been able to drive responsible, transparent, and explainable AI workflow to operationalize AI and mitigate risk and regulatory compliance easily.
Senior Data Analyst at Wipro
 

Customer Service

Sentiment score
6.3
Azure Data Factory support is generally satisfactory, with responsive assistance and strong community resources enhancing user satisfaction.
Sentiment score
7.7
IBM Cloud Pak for Data's support is responsive and available 24/7, despite some delays due to system complexity.
The technical support from Microsoft is rated an eight out of ten.
Chief Analytics Officer at Idiro Analytics
The technical support is responsive and helpful
Sr. Technical Architect at Hexaware Technologies Limited
The technical support for Azure Data Factory is generally acceptable.
Solution Architect at Mercedes-Benz AG
Cloud Pak is a complicated system, and it's often difficult to find the right resource in IBM to help with specific issues.
Data asset management engineer at a tech services company with 1-10 employees
I would rate IBM's support at about a seven or eight out of ten because we have good support coverage owing to our long association with IBM.
EDW Manager at a university with 1,001-5,000 employees
Customer support should be more responsive and reach and respond on time.
Senior Data Analyst at Wipro
 

Scalability Issues

Sentiment score
7.4
Azure Data Factory offers scalable cloud-based solutions for diverse operations, despite some third-party integration limitations and use case challenges.
Sentiment score
7.4
IBM Cloud Pak for Data is praised for impressive scalability and compatibility, despite being resource-intensive, with ratings mostly 8-10.
Azure Data Factory is highly scalable.
Chief Analytics Officer at Idiro Analytics
I have not noticed any downtime or lagging, especially when dealing with large data, so it is relatively very scalable.
Senior Data Analyst at Wipro
 

Stability Issues

Sentiment score
7.8
Azure Data Factory is considered highly stable and reliable, though minor issues can occur, mostly in development environments.
Sentiment score
7.5
IBM Cloud Pak for Data is generally stable and well-rated, with users emphasizing scalability despite minor tool integration concerns.
The solution has a high level of stability, roughly a nine out of ten.
Chief Analytics Officer at Idiro Analytics
 

Room For Improvement

Azure Data Factory needs better integration, UI, documentation, data handling, pricing transparency, real-time processing, connectivity, and scheduling.
IBM Cloud Pak for Data needs better integration, support, features, and pricing to attract small businesses and simplify adoption.
Incorporating more dedicated API sources to specific services like HubSpot CRM or Salesforce would be beneficial.
Chief Analytics Officer at Idiro Analytics
Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.
Sr. Technical Architect at Hexaware Technologies Limited
There is a problem with the integration with third-party solutions, particularly with SAP.
Solution Architect at Mercedes-Benz AG
Setting up the hybrid and multi-cloud environments is a long job and it takes time.
Senior Data Analyst at Wipro
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.
Data asset management engineer at a tech services company with 1-10 employees
I do not know if Cognos has all the features that users are looking for since we provide it as our standard and do not maintain infrastructure for other tools.
EDW Manager at a university with 1,001-5,000 employees
 

Setup Cost

Azure Data Factory's pricing is pay-as-you-go, with costs based on usage, offering competitive and cost-effective solutions.
IBM Cloud Pak for Data's high pricing and complexity make it more suitable for large enterprises than smaller companies.
The pricing is cost-effective.
Chief Analytics Officer at Idiro Analytics
It is considered cost-effective.
Sr. Technical Architect at Hexaware Technologies Limited
The setup cost is very expensive.
Data asset management engineer at a tech services company with 1-10 employees
Regarding my experience with pricing, setup cost, and licensing, for a small organization, the price might be relatively high, but for huge enterprises such as ours, the price is relatively affordable.
Senior Data Analyst at Wipro
 

Valuable Features

Azure Data Factory offers scalable ETL processes with easy integration, user-friendly interface, and strong orchestration, security, and automation features.
IBM Cloud Pak for Data excels with data virtualization, low-code navigation, robust security, and efficient data integration and collaboration.
It connects to different sources out-of-the-box, making integration much easier.
Sr. Technical Architect at Hexaware Technologies Limited
The platform excels in handling major datasets, particularly when working with Power BI for reporting purposes.
Data Engineer at Vthinktechnologies
Regarding the integration feature in Azure Data Factory, the integration part is excellent; we have major source connectors, so we can integrate the data from different data sources and also perform basic transformation while transforming, which is a great feature in Azure Data Factory.
Director at a computer software company with 1,001-5,000 employees
From there, I can work my way into a more granular level, applying all of that information on top of my actual data to understand what my data looks like, where it came from, and where it went wrong, managing it throughout the cycle.
Data asset management engineer at a tech services company with 1-10 employees
The benefits of choosing IBM Cognos, in addition to saving on cost, include having institutional knowledge about maintaining this infrastructure and enough people who have developed on Cognos in the past, which creates comfort in its use.
EDW Manager at a university with 1,001-5,000 employees
We have been able to save approximately 80 percent of our time. We are not doing data analysis manually, so this relieves our data department of dealing with data.
Senior Data Analyst at Wipro
 

Categories and Ranking

Azure Data Factory
Ranking in Data Integration
3rd
Average Rating
8.0
Reviews Sentiment
6.8
Number of Reviews
93
Ranking in other categories
Cloud Data Warehouse (2nd)
IBM Cloud Pak for Data
Ranking in Data Integration
23rd
Average Rating
8.0
Reviews Sentiment
6.5
Number of Reviews
15
Ranking in other categories
Data Virtualization (3rd)
 

Mindshare comparison

As of January 2026, in the Data Integration category, the mindshare of Azure Data Factory is 3.2%, down from 10.0% compared to the previous year. The mindshare of IBM Cloud Pak for Data is 1.3%, down from 1.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration Market Share Distribution
ProductMarket Share (%)
Azure Data Factory3.2%
IBM Cloud Pak for Data1.3%
Other95.5%
Data Integration
 

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.
Michelle Leslie - PeerSpot reviewer
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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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
11%
Manufacturing Company
9%
Government
7%
Financial Services Firm
26%
Manufacturing Company
10%
Computer Software Company
8%
Government
4%
 

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 Business7
Large Enterprise12
 

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 is your experience regarding pricing and costs for IBM Cloud Pak for Data?
The pricing and setup cost are handled by a different procurement team. Our IT procurement team is centralized, so licensing and the actual cost of the software are taken care of by a different tea...
What needs improvement with IBM Cloud Pak for Data?
I do not know if Cognos has all the features that users are looking for since we provide it as our standard and do not maintain infrastructure for other tools.
What is your primary use case for IBM Cloud Pak for Data?
The main use case for IBM Cognos is for business intelligence and reporting.
 

Also Known As

No data available
Cloud Pak for Data
 

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
Qatar Development Bank, GuideWell, Skanderborg Music Festival
Find out what your peers are saying about Azure Data Factory vs. IBM Cloud Pak for Data and other solutions. Updated: December 2025.
881,082 professionals have used our research since 2012.