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IBM Cloud Pak for Data vs Upsolver 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

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)
Upsolver
Ranking in Data Integration
38th
Average Rating
8.4
Reviews Sentiment
7.4
Number of Reviews
3
Ranking in other categories
Streaming Analytics (20th)
 

Mindshare comparison

As of February 2026, in the Data Integration category, the mindshare of IBM Cloud Pak for Data is 1.3%, down from 1.7% compared to the previous year. The mindshare of Upsolver is 0.7%, up from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration Market Share Distribution
ProductMarket Share (%)
IBM Cloud Pak for Data1.3%
Upsolver0.7%
Other98.0%
Data Integration
 

Featured Reviews

ArchanaSingh - PeerSpot reviewer
Senior Data Analyst at Wipro Limited
Collaborative data platform has transformed analytics and now drives faster decisions
The best features IBM Cloud Pak for Data offers include robust data visualization, centralized data analytics, data reliability, and compatibility with hybrid and multi-cloud environments. The compatibility with hybrid and multi-cloud environments has helped our organization as data visualization is very simple. Migrations, reading, analysis, and data management from other sources are performed without problems of requirements. We have a team of experts in IBM Cloud Pak for Data to maintain security and correct data management easily. I find this cloud excellent for visualizing and managing data across networks and also fulfilling fastest data storage, making it less complex and completely improving productivity in my organization. Everything is managed in multiple environments without any problem. IBM Cloud Pak for Data has positively impacted my organization, and I have noticed some improvement since we started using this tool. Configuration with hybrid and multi-cloud environments has been very seamless and easy. It is a robust platform capable of working with multiple data sources where we gain insights to make data-driven decisions easily. It automates data analysis for quick and better performance. We have seen improvements in analysis and data correction from multiple sources. Our productivity in the company is growing, thanks to the data analysis team. We have also seen a robust hybrid and multi-cloud access system working seamlessly. I can share specific outcomes that show how productivity has grown and how performance has improved since the data is automated, and the analysis is done much faster, saving us a lot of time. 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. We have been relieved of a lot of duties, and now we are able to focus on other strategic tasks. Our productivity has greatly increased since we are able to make concrete and data-driven decisions easily.
reviewer2784462 - PeerSpot reviewer
Software Engineer at a tech vendor with 10,001+ employees
Streaming pipelines have become simpler and onboarding new data sources is now much faster
One of the best features Upsolver offers is the automatic schema evolution. Another good feature is SQL-based streaming transformations. Complex streaming transformations such as cleansing, deduplication, and enrichment were implemented using SQL and drastically reduced the need for custom Spark code. My experience with the SQL-based streaming transformations in Upsolver is that it had a significant positive impact on the overall data engineering workflow. By replacing custom Spark streaming jobs with declarative SQL logic, I simplified development, review, and deployment processes. Data transformations such as parsing, filtering, enrichment, and deduplication could be implemented and modified quickly without rebuilding or redeploying complex code-based pipelines. Upsolver has impacted my organization positively because it brings many benefits. The first one is faster onboarding of new data sources. Another one is more reliable streaming pipelines. Another one is near-real-time data availability, which is very important for us. It also reduced operational effort for data engineering teams. A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days. Custom Spark code reduction reached 50 to 40 percent. Pipeline failures are reduced by 70 to 80 percent. Data latency is improved from hours to minutes.

Quotes from Members

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

Pros

"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."
"What I found most helpful in IBM Cloud Pak for Data is containerization, which means it's easy to shift and leave in terms of moving to other clouds. That's an advantage of IBM Cloud Pak for Data."
"Cloud Pak is a very, very, very good system."
"IBM Watson Catalog and data pipelines are the most valuable features of the solution."
"I would advise others looking into using IBM Cloud Pak for Data that it is a very great tool that is an all-in-one, real-time data analytics solution that provides a phenomenal user experience."
"One of Cloud Pak's best features is the Watson Knowledge Catalog, which helps you implement data governance."
"Its data preparation capabilities are highly valuable."
"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."
"Customer service is excellent, and I would rate it between eight point five to nine out of ten."
"A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days, custom Spark code reduction reached 50 to 40 percent, pipeline failures are reduced by 70 to 80 percent, and data latency is improved from hours to minutes."
"The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies."
"It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality."
 

Cons

"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."
"The interface could improve because sometimes it becomes slow. Sometimes there is a delay between clicks when using the software, which can make the development process slow. It can take a few seconds to complete one action, and then a few more seconds to do the next one."
"The solution could have more connectors."
"The setup for IBM Cloud Pak for Data is very complex, and our teams responsible for standing up the environment struggled a lot."
"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 two main challenges that I face are setup complexity and customer support responsiveness."
"The technical support could be a little better."
"On the stability side, I would rate it seven out of ten. Using multiple cloud providers and data engineering technologies creates complexity, and managing different plugins is not always easy, but they are working on it."
"I think that Upsolver can be improved in orchestration because it is not a full orchestration tool."
"Upsolver excels in ETL and data aggregation, while ThoughtSpot is strong in natural language processing for querying datasets. Combining these tools can be very effective: Upsolver handles aggregation and ETL, and ThoughtSpot allows for natural language queries. There’s potential for highlighting these integrations in the future."
"There is room for improvement in query tuning."
 

Pricing and Cost Advice

"The solution is expensive."
"Cloud Pak's cost is a little high."
"It's quite expensive."
"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."
"The solution's pricing is competitive with that of other vendors."
"I think that this product is too expensive for smaller companies."
"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."
"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."
"Upsolver is affordable at approximately $225 per terabyte per year."
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Top Industries

By visitors reading reviews
Financial Services Firm
26%
Manufacturing Company
9%
Computer Software Company
8%
Government
4%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business7
Large Enterprise12
No data available
 

Questions from the Community

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.
What is your experience regarding pricing and costs for Upsolver?
My experience with pricing, setup cost, and licensing was a very good experience, but it is not a direct experience because it was not my responsibility. It was in charge of the customer. However, ...
What needs improvement with Upsolver?
I think that Upsolver can be improved in orchestration because it is not a full orchestration tool. I believe it could be better in this regard. The cost needs attention at a very large scale. I th...
What is your primary use case for Upsolver?
My main use case for Upsolver is during an IT consulting project for a large enterprise running a cloud-native data platform on AWS. I used Upsolver to ingest and process high-volume stream data fr...
 

Also Known As

Cloud Pak for Data
No data available
 

Overview

 

Sample Customers

Qatar Development Bank, GuideWell, Skanderborg Music Festival
Information Not Available
Find out what your peers are saying about IBM Cloud Pak for Data vs. Upsolver and other solutions. Updated: February 2026.
881,707 professionals have used our research since 2012.