StreamSets vs Upsolver comparison

IBM and Upsolver are both solutions in the Data Integration category. IBM is ranked #23 with an average rating of 9.5, while Upsolver is ranked #36 with an average rating of 8.5. IBM holds a 1.2% mindshare in DI, compared to Upsolver’s 0.7% mindshare. Additionally, 100% of IBM users are willing to recommend the solution, compared to 100% of Upsolver users who would recommend it.

StreamSets

Read 21 StreamSets reviews

3,524 Views
2,643 Comparison Views

100% willing to recommend

Upsolver

Read 4 Upsolver reviews

1,396 Views
1,131 Comparison Views

100% willing to recommend

StreamSets

Upsolver

Comparison Buyer's Guide

Download the report

Executive Summary

We performed a comparison between StreamSets and Upsolver based on real PeerSpot user reviews.

Find out in this report how the two Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed StreamSets vs. Upsolver Report (Updated: April 2026).

Buyer's Guide

StreamSets vs. Upsolver

April 2026

Download the complete report

Helped 894,738 peers since 2012

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

StreamSets

Ranking in Data Integration

23rd

Average Rating

8.4

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Upsolver

Ranking in Data Integration

36th

Average Rating

8.6

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

Streaming Analytics (20th)

Mindshare comparison

As of May 2026, in the Data Integration category, the mindshare of StreamSets is 1.2%, down from 1.6% 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 Mindshare Distribution
Product	Mindshare (%)
StreamSets	1.2%
Upsolver	0.7%
Other	98.1%

Data Integration

Featured Reviews

SrinivasanSankar

Enterprise Solutions Architect at a energy/utilities company with 1,001-5,000 employees

Enables effective batch loading with visual interface and enterprise support

One issue I observed with StreamSets is that the memory runs out quickly when processing large volumes of data. Because of this memory issue, we have to upgrade our EC2 boxes in the Amazon AWS infrastructure. I had to switch to a new EC2 box, even though the processor was not fully utilized. It would be beneficial if StreamSets addressed any potential memory leak issues to prevent unnecessary upgrades. Additionally, it would be a great enhancement if StreamSets could produce a lineage graph to visualize how the data has passed through the system.

Read full review

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.

Read full review

Quotes from Members

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

Pros

"The ETL capabilities are very useful for us. We extract and transform data from multiple data sources, into a single, consistent data store, and then we put it in our systems. We typically use it to connect our Apache Kafka with data lakes. That process is smooth and saves us a lot of time in our production systems."

"The most valuable features are the option of integration with a variety of protocols, languages, and origins."

"The ability to have a good bifurcation rate and fewer mistakes is valuable."

"One of the things I like is the data pipelines. They have a very good design. Implementing pipelines is very straightforward. It doesn't require any technical skill."

"This product was a lot easier to use than the one we had before it, and it took us half an hour and we were set up and running it the first time."

"It has helped us in moving into the data analytics world at a faster pace than any other tool would have done."

"The most valuable would be the GUI platform that I saw. I first saw it at a special session that StreamSets provided towards the end of the summer. I saw the way you set it up and how you have different processes going on with your data. The design experience seemed to be pretty straightforward to me in terms of how you drag and drop these nodes and connect them with arrows."

"The most valuable feature is the pipelines because they enable us to pull in and push out data from different sources and to manipulate and clean things up within them."

More StreamSets pros

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

"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."

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

Cons

"One issue I observed with StreamSets is that the memory runs out quickly when processing large volumes of data. Because of this memory issue, we have to upgrade our EC2 boxes in the Amazon AWS infrastructure."

"The logging mechanism could be improved. If I am working on a pipeline, then create a job out of it and it is running, it will generate constant logs. So, the logging mechanism could be simplified. Now, it is a bit difficult to understand and filter the logs. It takes some time."

"They need to improve their customer care services. Sometimes it has taken more than 48 hours to resolve an issue. That should be reduced. They are aware of small or generic issues, but not the more technical or deep issues. For those, they require some time, generally 48 to 72 hours to respond. That should be improved."

"In terms of the product, I don't think there is any room for improvement because it is very good. One small area of improvement that is very much needed is on the knowledge base side. Sometimes, it is not very clear how to set up a certain process or a certain node for a person who's using the platform for the first time."

"One area for improvement could be the cloud storage server speed, as we have faced some latency issues here and there."

"The logging mechanism could be improved. Now, it is a bit difficult to understand and filter the logs."

"I would like to see it integrate with other kinds of platforms, other than Java. We're going to have a lot of applications using .NET and other languages or frameworks. StreamSets is very helpful for the old Java platform but it's hard to integrate with the other platforms and frameworks."

"If you use JDBC Lookup, for example, it generally takes a long time to process data."

More StreamSets cons

"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."

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

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

"There is room for improvement in query tuning."

Pricing and Cost Advice

"The pricing is too fixed. It should be based on how much data you need to process. Some businesses are not so big that they process a lot of data."

"StreamSets is an expensive solution."

"It's not so favorable for small companies."

"StreamSets Data Collector is open source. One can utilize the StreamSets Data Collector, but the Control Hub is the main repository where all the jobs are present. Everything happens in Control Hub."

"It has a CPU core-based licensing, which works for us and is quite good."

"We are running the community version right now, which can be used free of charge."

"The overall cost is very flexible so it is not a burden for our organization... However, the cost should be improved. For small and mid-size organizations it might be a challenge."

"There are different versions of the product. One is the corporate license version, and the other one is the open-source or free version. I have been using the corporate license version, but they have recently launched a new open-source version so that anybody can create an account and use it. The licensing cost varies from customer to customer. I don't have a lot of input on that. It is taken care of by PMO, and they seem fine with its pricing model. It is being used enterprise-wide. They seem to have got a good deal for StreamSets."

More StreamSets pricing and cost advice

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

See which vendors are best for you

Use our free recommendation engine to learn which Data Integration solutions are best for your needs.

See recommendations

894,738 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

11%

Insurance Company

Manufacturing Company

Real Estate/Law Firm

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	2
Large Enterprise	11

No data available

Questions from the Community

What needs improvement with StreamSets?

See all answers

What is your primary use case for StreamSets?

We are using StreamSets for batch loading.

See all answers

What advice do you have for others considering StreamSets?

If asked, I definitely recommend StreamSets to other users. My overall rating for the solution is nine.

See all answers

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, ...

See all answers

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...

See all answers

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...

See all answers

Comparisons

Pentaho Data Integration and Analytics vs StreamSets

Compared 8% of the time

SSIS vs StreamSets

Compared 6% of the time

Confluent vs StreamSets

Compared 5% of the time

Informatica PowerCenter vs StreamSets

Compared 5% of the time

Matillion Data Productivity Cloud vs StreamSets

Compared 4% of the time

More StreamSets Competitors

Databricks vs Upsolver

Compared 12% of the time

Striim vs Upsolver

Compared 10% of the time

IBM Cloud Pak for Data vs Upsolver

Compared 8% of the time

SAS Access vs Upsolver

Compared 8% of the time

Alteryx Designer vs Upsolver

Compared 8% of the time

More Upsolver Competitors

Product Reports

Buyer's Guide

StreamSets

April 2026

Download StreamSets product report

Buyer's Guide

Data Integration

May 2026

Download Upsolver product report

Overview

StreamSets streamlines data pipeline creation, connecting data from multiple sources to destinations like cloud platforms with minimal coding. Its centralized platform and intuitive design enhance ETL and data migration processes.

StreamSets integrates seamlessly with analytics platforms, offering tools such as Data Collector and Control Hub to facilitate data ingestion, transformation, and machine learning integrations. Its user-friendly interface and ready connectors aid in configuring complex data pipelines. With built-in data drift resilience and scheduling options, users experience efficient, scalable data management, despite challenges like latency in cloud storage and interface enhancement needs. Users often employ StreamSets for batch loading, real-time data processing, and smart data pipeline management, offering comprehensive data integration solutions.

What are the key features of StreamSets?

Data Collector: Enables streamlined data collection and processing.
Control Hub: Centralizes management of data pipelines.
Minimal Coding: Simplifies pipeline configuration with limited code requirement.
Data Drift Resilience: Adapts to changes in data patterns effectively.

What benefits should users look for?

Time-Saving: Efficient processes reduce manual effort.
Scalability: Easily manages large datasets.
Integration: Connects with leading analytics and cloud platforms.
Real-Time Processing: Supports continuous data delivery for timely insights.

In industries like finance and technology, StreamSets supports data migration, machine learning integrations, and analytics by simplifying data transformation and enhancing decision-making capabilities through its robust pipeline management.

IBM

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

Availity, BT Group, Humana, Deluxe, GSK, RingCentral, IBM, Shell, SamTrans, State of Ohio, TalentFulfilled, TechBridge

Information Not Available

Buyer's Guide

StreamSets vs. Upsolver

April 2026

Free Report: StreamSets vs. Upsolver

Find out what your peers are saying about StreamSets vs. Upsolver and other solutions. Updated: April 2026.

DOWNLOAD NOW

894,738 professionals have used our research since 2012.

See our StreamSets vs. Upsolver report.

See our list of best Data Integration vendors.

We monitor all Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.