Qlik Talend Cloud vs Upsolver comparison

Why PeerSpot?

Qlik and Upsolver are both solutions in the Data Integration category. Qlik is ranked #7 with an average rating of 7.9, while Upsolver is ranked #41 with an average rating of 8.7. Qlik holds a 2.5% mindshare in DI, compared to Upsolver’s 0.7% mindshare. Additionally, 89% of Qlik users are willing to recommend the solution, compared to 100% of Upsolver users who would recommend it.

Qlik Talend Cloud

Read 56 Qlik Talend Cloud reviews

21,375 Views
8,550 Comparison Views

89% willing to recommend

Upsolver

Read 4 Upsolver reviews

2,687 Views
2,096 Comparison Views

100% willing to recommend

Qlik Talend Cloud

Upsolver

Comparison Buyer's Guide

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Executive SummaryUpdated on Feb 22, 2026

Qlik Talend Cloud and Upsolver are products competing in the data integration and management category. Qlik Talend Cloud seems to have the upper hand in customer service and pricing, whereas Upsolver is known for its robust features and performance capabilities.

Features: Qlik Talend Cloud offers extensive data transformation capabilities and strong integration options, streamlining complex workflows which are perfect for diverse enterprise needs. It impresses with its comprehensive integration ecosystem, making it easy to connect with various platforms. It is also equipped with powerful analytics tools enabling in-depth data insights. Upsolver stands out in real-time data processing and scalability, ideal for handling large data sets efficiently. Its advanced querying capabilities offer deep dives into real-time data, making it a preferred option for dynamic environments. The seamless handling of structured and unstructured data further enhances its performance.

Ease of Deployment and Customer Service: Qlik Talend Cloud is recognized for its user-friendly deployment model and excellent customer service support which ensures smoother implementation and ongoing assistance with minimal downtime. Its detailed documentation and responsive support services are typically rated highly by users. Upsolver provides straightforward deployment, facilitating quick setup and integration in technical environments. However, the service quality is considered less distinguished, which can impact long-term customer satisfaction.

Pricing and ROI: Qlik Talend Cloud is notable for its competitive pricing structure, which maximizes return on investment through cost efficiency, appealing to budget-conscious buyers. Its pricing plan typically cuts down on unnecessary expenses while providing exceptional value. Upsolver, on the other hand, has a higher setup cost but justifies this with superior data handling capabilities and performance efficiencies, targeting those who value high-speed processing and real-time handling despite the upfront expense. Companies that need robust, high-volume data processing might find the initial investment worthwhile.

To learn more, read our detailed Qlik Talend Cloud vs. Upsolver Report (Updated: October 2026).

Buyer's Guide

Qlik Talend Cloud vs. Upsolver

October 2026

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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

Qlik Talend Cloud

Ranking in Data Integration

7th

Ranking in Streaming Analytics

11th

Average Rating

8.0

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

Data Quality (2nd), Data Scrubbing Software (1st), Master Data Management (MDM) Software (4th), Cloud Data Integration (6th), Data Governance (8th), Cloud Master Data Management (MDM) (3rd), Integration Platform as a Service (iPaaS) (6th)

Upsolver

Ranking in Data Integration

41st

Ranking in Streaming Analytics

22nd

Average Rating

8.6

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2026, in the Data Integration category, the mindshare of Qlik Talend Cloud is 2.5%, up from 1.8% compared to the previous year. The mindshare of Upsolver is 0.7%, up from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Mindshare Distribution
Product	Mindshare (%)
Qlik Talend Cloud	2.5%
Upsolver	0.7%
Other	96.8%

Data Integration

Featured Reviews

Henrique Fsantos

Data & Analytics Engineer at PicPay

Automated data pipelines have improved analytics workflows and provide trusted reporting data

The best features of Qlik Talend Cloud are its connectors to many systems, databases, APIs, and files, including both SQL and NoSQL databases. The scheduling feature allows me to run jobs automatically in the cloud, and the built-in data quality tools help clean and check data. The reusable components enable me to reuse jobs and parts, which saves time, making it easy to build good data pipelines. The feature that has made the biggest difference for me in Qlik Talend Cloud is the scheduling and automation, which helps me run ETL jobs automatically without manual work. This saves time, reduces errors, and makes the pipelines more stable. In my daily work, I can trust that the data is updated on time and ready for reports and analytics. Qlik Talend Cloud has had a positive impact on my organization by automating data integration processes, reducing manual errors, and ensuring reliable and up-to-date data for reports and business decisions. This has improved operational stability, productivity, and trust in the data. Since using Qlik Talend Cloud, we have reduced manual work, resulting in approximately 20% to 30% time savings in data load processes. We have also seen a reduction in operational errors, mainly due to automation and the data quality checks, and a clear improvement in data reliability since the pipelines became more standardized and monitored.

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reviewer2784462

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.

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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

"It offers advanced features that allow you to create custom patterns and use regular expressions to identify data issues."

"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."

"I think Talend is one of the easiest tools for faster implementation compared to other tools."

"The file fetch process is impeccable."

"We are able to get emails from URLs very easily using this function when others fail."

"Overall, from 2017 to 2020, we have almost saved around 140,000 to 160,000 hours, which is only with respect to the data."

"I like idea of storing the results of Data Quality jobs in a DB and having the ability to run reports in the DB to show a dashboard of quality metrics."

"Some of the algorithms that are inbuilt in Talend Data Quality, such as Levenshtein, are the most valuable functions for us."

More Qlik Talend Cloud pros

"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."

"Customer service is excellent, and I would rate it between eight point five to nine out of ten."

"I have saved 50 to 60% on maintaining pipelines since using Upsolver."

"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

"They don't have any AI capabilities. Talend DQ is specifically for data quality, which only has data profiling. With Talend DQ, I cannot generate any reports today, so I need an ETL tool. It provides general Excel files, or I have to create some views. If instead of buying a new tool, Talend provides a reporting capability or solution, it would be great. It will reduce the development effort for creating these kinds of reports. We also manage the infrastructure for Talend. From the licensing perspective, for cloud, they only have seat licenses where one person is tied to one license, but for on-premise, they have concurrent licenses. It would be really awesome if they can provide concurrent licenses for the cloud so that if one person is not there, somebody else can use that license. Currently, it is not possible unless a person deactivates his or her license and moves the same seat license to someone else. We are one of the biggest customers in the central zone of the US for Talend, and this is the feedback that we have provided them again and again, but they come back and say that they aren't able to provide concurrent licenses on the cloud. In version 7.3, there is a feature for tokenization and de-tokenization of data. This is the feature that we are looking for. It is useful if somebody wants to see what we have masked and how do we demask it. This feature is not there in version 7.1. There are also a few other capabilities on the cloud, but we don't yet have a big footprint in the cloud."

"Performance and speed could be improved."

"Finding assistance with issues can be spotty. With Python, there are literally millions of open source answers which are recent and apply to the version that we are using."

"Including either XML or JSON in the next release would definitely be a good transformation. I'm not sure if Talend has that feature, but it's one of those requirements that we are working around and have to do some parsing of XML so this could make it easier."

"When we upgraded to Version 6.4.1, we tried using a GIT repository instead of a SVN repository. After a few incidents where things disappeared and changes were not saved, we decided to go back to a SVN repository."

"For us in New Zealand, it is quite expensive."

"You can't join more than two tables for analysis."

"Sometimes there are bugs which are unidentified and we have to follow-up with the Talend team to resolve them. In a critical situation, it takes time for them to update patches."

More Qlik Talend Cloud cons

"I think that Upsolver can be improved in orchestration because it is not a full orchestration tool."

"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."

"I would say Upsolver's scalability is eight out of 10 because of pricing."

"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

"I have been using the open-source version."

"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."

"It's a subscription-based platform, we renew it every year."

"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."

"Moreover, the pricing structure stands out as highly competitive compared to other offerings in the market, making it a cost-effective choice for users."

"The price of the Talend Data Management Platform is reasonable. The other competing solutions are priced high. Gartner Magic Quadrant identified other solutions, such as Informatica, that are far more expensive."

"The licensing cost is about 40,000 Euros a year."

"I would advise to first take a look and at the Open Studio edition. Figure out what you need and purchase the appropriate license."

More Qlik Talend Cloud pricing and cost advice

"Upsolver is affordable at approximately $225 per terabyte per year."

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Top Industries

By visitors reading reviews

Financial Services Firm

14%

Outsourcing Company

12%

Comms Service Provider

11%

Construction Company

Real Estate/Law Firm

13%

Manufacturing Company

13%

Retailer

12%

Comms Service Provider

10%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	21
Midsize Enterprise	12
Large Enterprise	20

No data available

Questions from the Community

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 ...

See all answers

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...

See all answers

What advice do you have for others considering Talend Data Quality?

Currently, I'm working with batch jobs and don't perform real-time data quality monitoring because of the large data volume. For real-time, we use a different product. I cannot provide details abou...

See all answers

What is your experience regarding pricing and costs for Upsolver?

My experience with pricing, setup cost, and licensing is that the pricing is nine out of 10.

See all answers

What needs improvement with Upsolver?

I think Upsolver can be improved with deeper integration with external orchestration out of the box. I would appreciate more clear dashboards with billing in real time as a needed improvement.

See all answers

What is your primary use case for Upsolver?

My main use case for Upsolver is to operate with changes in the structure of new data without a pipeline disrupting. I write SQL queries in Upsolver, and the platform takes care of the data itself,...

See all answers

Comparisons

Informatica Intelligent Data Management Cloud (IDMC) vs Qlik Talend Cloud

Compared 3% of the time

Databricks vs Qlik Talend Cloud

Compared 2% of the time

Ataccama ONE Platform vs Qlik Talend Cloud

Compared 2% of the time

SSIS vs Qlik Talend Cloud

Compared 2% of the time

IBM webMethods Hybrid Integration vs Qlik Talend Cloud

Compared 2% of the time

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Databricks vs Upsolver

Compared 10% of the time

Striim vs Upsolver

Compared 10% of the time

Rivery vs Upsolver

Compared 8% of the time

Alteryx One vs Upsolver

Compared 6% of the time

SAS Access vs Upsolver

Compared 5% of the time

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Product Reports

Buyer's Guide

Qlik Talend Cloud

October 2026

Download Qlik Talend Cloud product report

Buyer's Guide

Data Integration

October 2026

Download Upsolver product report

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

Qlik Talend Cloud provides robust data integration tools tailored for efficient management of large volumes, offering real-time data access, Java integration, and custom code capabilities for developers.

Qlik Talend Cloud is known for its extensive connectivity options, enabling seamless integration across different platforms, such as S3, Redshift, Oracle, and SQL Server. The central repository facilitates consistent metadata access throughout organizations, enhancing collaboration. Despite its strengths in advanced monitoring, automation, and user-friendly drag-and-drop interfaces, users face challenges with installation stability, technical support, documentation inconsistencies, and complexities in learning. Performance concerns also include multitasking limitations and excessive memory usage. The platform's licensing costs can be prohibitive for smaller companies, while demands for improved data governance and intuitive code management continue. Its applications in healthcare data parsing, ETL task automation, and diverse data platform integration demonstrate its utility, although there's a constant demand for better scalability and efficient transformations.

What are the key features?

Seamless Real-Time Data Access: Ensures that users can access the latest information across systems without delays for better decision-making.
Java Integration: Facilitates smooth incorporation of Java components into workflows, broadening development options.
Custom Code Development: Offers flexibility to develop tailored solutions to meet specific integration scenarios.
Vast Connectors: Enhances connectivity between multiple platforms, supporting comprehensive data integration strategies.
Central Repository: Allows for uniform metadata access across departments, promoting cohesive data management practices.
Automation and Monitoring: Streamlines workflows with advanced automation and monitoring, reducing manual intervention and errors.

What benefits and ROI should users evaluate?

Improved Data Quality: Tools for data matching and error handling ensure high-quality, reliable data for analytics and reporting.
Efficient Workflow Streamlining: Drag-and-drop interfaces and automation features simplify complex workflows, enhancing operational efficiency.
Enhanced Business Insights: By integrating diverse data sources, businesses can derive deeper insights and ensure compliance through better data management.
Cost Management: Although some users find licensing expensive, comprehensive feature sets aimed at improving data processes can justify costs.
Community Support: Users benefit from active community forums that provide assistance, although technical support also necessitates enhancements.

In specialized industries like healthcare, users leverage Qlik Talend Cloud for data integration and transformation, aiding in compliance and analytics. Compatibility with cloud and on-premises systems ensures adaptability to complex data tasks, facilitating business application development. Organizations focus on enhanced data ingestion and quality checks for comprehensive solutions.

Qlik

Upsolver offers a data lakehouse platform that simplifies big data processing and analytics, enabling data teams to efficiently handle large-scale datasets.

Upsolver's platform focuses on simplifying the complexities of data engineering by transforming raw data into queryable formats quickly. It allows seamless integration with cloud storage and various database systems, providing flexibility for data-driven businesses. The platform automates data preparation tasks, reducing manual coding and allowing teams to focus on extracting insights. Its scalable architecture supports real-time analytics and batch processing.

What are the key features of Upsolver?

Data Ingestion: Easily ingest data from multiple sources with minimal configuration.
Schema On-Read: Automatically interpret data schemas, simplifying data management.
Streaming SQL: Transform and analyze streaming data using a familiar SQL syntax.
Data Lake Integration: Seamlessly connect with data lakes for streamlined workflows.

What benefits and ROI should users consider?

Time Savings: Faster data processing reduces time to insights.
Cost Efficiency: Scalable architecture optimizes infrastructure costs.
Scalability: Handle growing data volumes effortlessly.
Flexibility: Supports wide data storage and processing strategies.

In the e-commerce industry, Upsolver helps businesses optimize their data pipelines for better customer segmentation and personalization, while in finance, it enhances fraud detection capabilities through real-time data analytics.

Upsolver

Sample Customers

Aliaxis, Electrocomponents, M¾NCHENER VEREIN, The Sunset Group

Information Not Available

Buyer's Guide

Qlik Talend Cloud vs. Upsolver

October 2026

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