Try our new research platform with insights from 80,000+ expert users

Qlik Compose 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

Qlik Compose
Ranking in Data Integration
45th
Average Rating
7.6
Reviews Sentiment
6.5
Number of Reviews
12
Ranking in other categories
No ranking in other categories
Upsolver
Ranking in Data Integration
37th
Average Rating
8.4
Reviews Sentiment
7.4
Number of Reviews
3
Ranking in other categories
Streaming Analytics (20th)
 

Mindshare comparison

As of January 2026, in the Data Integration category, the mindshare of Qlik Compose is 0.9%, down from 1.1% 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 (%)
Upsolver0.6%
Qlik Compose0.9%
Other98.5%
Data Integration
 

Featured Reviews

Sahil Taneja - PeerSpot reviewer
Principal Consultant/Manager at Tenzing
Easy matching and reconciliation of data
The initial setup was easy for the data warehousing concept. But for a person who is new to ETL and warehousing concepts, it may take some time. If someone is familiar with these concepts, they could understand and learn the tool quickly. However, compared to other tools, the UI is complex. It would be helpful to have a better UI and documentation for new users. As of now, there is a challenge in learning the Compose tool for new users altogether. Qlik Compose was deployed on-premises. But the servers, like the SQL servers were maintained on the cloud—the managed instances.
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 have found it to be a very good, stable, and strong product."
"As long as you pick the solution that best fits with your requirements, you won't find that performance is a problem. It's good."
"The most valuable is its excellence as a graphical data representation tool and the versatility it offers, especially with drill-down capabilities."
"One of the most valuable features was the ability to integrate multiple source systems that mainly used structured IDBMS versions."
"The technical support is very good. I rate the technical support a ten out of ten."
"One of the most valuable features of this tool is its automation capabilities, allowing us to design the warehouse in an automated manner. Additionally, we can generate Data Lifecycle Policies (DLP) reports and efficiently implement updates and best practices based on proven design patterns."
"I like modeling and code generation. It has become a pretty handy tool because of its short ideation to delivery time. From the time you decide you are modeling a data warehouse, and once you finish the modeling, it generates all the code, generates all the tables. All you have to do is tick a few things, and you can produce a fully functional warehouse. I also like that they have added all the features I have asked for over four years."
"It can scale."
"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."
"It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality."
"The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies."
 

Cons

"I'd like to have access to more developer training materials."
"There should be proper documentation available for the implementation process."
"Qlik's ETL and data transformation could be better."
"There is some scope for improvement around the documentation, and a better UI would definitely help."
"For more complex work, we are not using Qlik Compose because it cannot handle very high volumes at the moment. It needs the same batching capabilities that other ETL tools have. We can't batch the data into small chunks when transforming large amounts of data. It tries to do everything in one shot and that's where it fails."
"I believe that visual data flow management and the transformation function should be improved."
"When processing data from certain tables with a large volume of data, we encounter significant delays. For instance, when dealing with around one million records, it typically takes three to four hours. To address this, I aim to implement performance improvements across all tables, ensuring swift processing similar to those that are currently complete within seconds. The performance issue primarily arises when we analyze the inserts and updates from the source, subsequently dropping the table. While new insertions are handled promptly, updates are processed slowly, leading to performance issues. Despite consulting our Qlik vendors, they were unable to pinpoint the exact cause of this occurrence. Consequently, I am seeking ways to optimize performance within Qlik Compose, specifically concerning updates."
"My issues with the solution's stability are owing to the fact that it has certain bugs causing issues in some functionalities that should be working."
"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."
"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."
 

Pricing and Cost Advice

"The price of the solution is expensive."
"On a scale of one to ten, where one is cheap, and ten is very expensive, I rate the solution a six."
"While they outperform Tableau, there's room for improvement in Qlik's pricing structures, especially for corporate clients like us."
"Upsolver is affordable at approximately $225 per terabyte per year."
report
Use our free recommendation engine to learn which Data Integration solutions are best for your needs.
881,082 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
15%
Government
12%
Manufacturing Company
9%
Insurance Company
7%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise3
Large Enterprise6
No data available
 

Questions from the Community

Which ETL tool would you recommend to populate data from OLTP to OLAP?
There are two products I know about * TimeXtender : Microsoft based, Transformation logic is quiet good and can easily be extended with T-SQL , Has a semantic layer that generates metat data for cu...
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

Compose, Attunity Compose
No data available
 

Overview

 

Sample Customers

Poly-Wood
Information Not Available
Find out what your peers are saying about Qlik Compose vs. Upsolver and other solutions. Updated: December 2025.
881,082 professionals have used our research since 2012.