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Qlik Talend Cloud vs Upsolver comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Nov 18, 2025

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

Qlik Talend Cloud
Ranking in Data Integration
9th
Ranking in Streaming Analytics
9th
Average Rating
8.0
Reviews Sentiment
6.5
Number of Reviews
54
Ranking in other categories
Data Quality (3rd), Data Scrubbing Software (1st), Master Data Management (MDM) Software (3rd), Cloud Data Integration (7th), Data Governance (8th), Cloud Master Data Management (MDM) (4th), Integration Platform as a Service (iPaaS) (9th)
Upsolver
Ranking in Data Integration
37th
Ranking in Streaming Analytics
20th
Average Rating
8.4
Reviews Sentiment
7.4
Number of Reviews
3
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of January 2026, in the Data Integration category, the mindshare of Qlik Talend Cloud is 1.9%, down from 2.1% compared to the previous year. The mindshare of Upsolver is 0.6%, 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 (%)
Qlik Talend Cloud1.9%
Upsolver0.6%
Other97.5%
Data Integration
 

Featured Reviews

HJ
IT Consultant at a tech services company with 201-500 employees
Has automated recurring data flows and improved accuracy in reporting
The best features of Talend Data Integration are its rich set of components that let you connect to almost any data design intuitive and its strong automation and scheduling capabilities. The TMap component is especially valuable because it allows flexible transformation, joins, and filtering in a single place. I also rely a lot on context variables to manage different environments like Dev, Test, and production, without changing the code. The error handling and logging tools are very helpful for monitoring and troubleshooting, which makes the workflow more reliable. Talend Data Integration has helped our company by automating and standardizing data processes. Before, many of these tasks were done manually, which took more time and often led to errors. With Talend Data Integration, we built automated pipelines that extract, clean, and load data consistently. This not only saves hours of manual effort, but also improves the accuracy and reliability of data. As a result, business teams had faster access to trustworthy information for reporting and decision making, which directly improved efficiency and productivity. Talend Data Integration has had a measurable impact on our organization. By automating daily data loading processes, we reduced manual effort by around three or four hours per day, which saved roughly 60 to 80 hours per month. We also improved data accuracy. Error rates dropped by more than 70% because validation rules were built into the jobs. In addition, reporting teams now receive fresh data at least 50% faster, which means they can make decisions earlier and with more confidence. Overall, Talend Data Integration has increased both efficiency and reliability in our data workflows.
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

"The solution can run on any machine and that is a big advantage."
"The best feature of Talend Data Integration is its multiple data DB components; we have almost all the components and also cloud versions, with TMC allowing us to perform data preparation and data stewardship."
"Talend's most valuable feature is its ability to generate code and packages efficiently."
"I like everything about this product, but the biggest thing is the ease of use."
"The most valuable feature lies in the capability to assign data quality issues to different stakeholders, facilitating the tracking and resolution of defective work."
"They're very competitive in terms of performance, which is a good selling point. It has very rich features. It provides a very rich feature set in the application."
"The availability of connectors is great."
"The product's integration with PostgreSQL and Jira has been helpful for us. Its performance is good. However, we do not use it for large data sets."
"The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies."
"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."
"It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality."
"Customer service is excellent, and I would rate it between eight point five to nine out of ten."
 

Cons

"If I am designing something in Talend Data Integration, I cannot backtrack in the flow. For example, if I read data from a source and transform it before applying complex aggregation in the third step, I cannot join this data with the second step."
"The documentation from version to version could be more accurate."
"The ability to change the code when debugging the JavaScript could be improved."
"I would say that some of the support elements need improvement."
"Data management could also encompass APIs and real-time streaming data integration."
"Talend Data Integration can be improved by reducing the license cost, as it is a bit high compared to other tools, which can be a burden for small-scale companies wanting to buy a license."
"In terms of the solution's technical support, the interactions were satisfactory, but there is room for improvement, especially in managing expectations."
"I've had some issues with bugs causing crashes, especially when making changes to the system or with the monthly upgrades to Studio they've introduced."
"I think that Upsolver can be improved in orchestration because it is not a full orchestration tool."
"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."
"There is room for improvement in query tuning."
"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."
 

Pricing and Cost Advice

"The solution's pricing is very reasonable and half the cost of Informatica."
"The licensing cost is about 40,000 Euros a year."
"It is cheaper than Informatica. Talend Data Quality costs somewhere between $10,000 to $12,000 per year for a seat license. It would cost around $20,000 per year for a concurrent license. It is the same for the whole big data solution, which comes with Talend DI, Talend DQ, and TDM."
"The price is on a per-user basis. It's a little more expensive than other tools. There aren't any additional costs beyond the standard licensing fee."
"The tool is cheap."
"I would advise to first take a look and at the Open Studio edition. Figure out what you need and purchase the appropriate license."
"It's a subscription-based platform, we renew it every year."
"The pricing is a little higher than what I had expected, but it's comparable with I-PASS competitors."
"Upsolver is affordable at approximately $225 per terabyte per year."
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Top Industries

By visitors reading reviews
Financial Services Firm
12%
Computer Software Company
10%
Comms Service Provider
7%
Manufacturing Company
7%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business20
Midsize Enterprise11
Large Enterprise20
No data available
 

Questions from the Community

What do you like most about Talend Data Quality?
The most valuable feature lies in the capability to assign data quality issues to different stakeholders, facilitating the tracking and resolution of defective work.
What needs improvement with Talend Data Quality?
I don't use the automated rule management feature in Talend Data Quality that much, so I cannot provide much feedback. I may not know what Talend Data Quality can improve for data quality. I'm not ...
What is your primary use case for Talend Data Quality?
It is for consistency, mainly; data consistency and data quality are our main use cases for the product. Data consistency is the primary purpose we use it for, as we have written rules in Talend Da...
What is your experience regarding pricing and costs for Upsolver?
Upsolver is affordable at approximately $225 per terabyte per year. Compared to what I know from others, it's cheaper than many other products.
What needs improvement with Upsolver?
There is room for improvement in query tuning. Upsolver could do a more in-depth analysis in employing machine power, such as CPU and memory, to enhance query performance. Furthermore, allocating C...
What is your primary use case for Upsolver?
I am working as a consultant and currently have my own consultancy services. I provide services to companies that are data-heavy and looking for data engineering solutions for their business needs....
 

Also Known As

Talend Data Quality, Talend Data Management Platform, Talend MDM Platform, Talend Data Streams, Talend Data Integration, Talend Data Integrity and Data Governance
No data available
 

Overview

 

Sample Customers

Aliaxis, Electrocomponents, M¾NCHENER VEREIN, The Sunset Group
Information Not Available
Find out what your peers are saying about Qlik Talend Cloud vs. Upsolver and other solutions. Updated: January 2026.
881,082 professionals have used our research since 2012.