StreamSets vs Upsolver comparison

IBM and Upsolver are both solutions in the Data Integration category. IBM is ranked #24 with an average rating of 9.5, while Upsolver is ranked #38 with an average rating of 8.5. IBM holds a 1.2% mindshare in DI, compared to Upsolver’s 0.8% 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

3,998 Views
3,038 Comparison Views

100% willing to recommend

Upsolver

Read 4 Upsolver reviews

1,819 Views
1,455 Comparison Views

100% willing to recommend

StreamSets

Upsolver

Comparison Buyer's Guide

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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: June 2026).

Buyer's Guide

StreamSets vs. Upsolver

June 2026

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Helped 902,988 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

24th

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

38th

Average Rating

8.6

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

Streaming Analytics (21st)

Mindshare comparison

As of July 2026, in the Data Integration category, the mindshare of StreamSets is 1.2%, down from 1.5% compared to the previous year. The mindshare of Upsolver is 0.8%, 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	0.8%
Other	98.0%

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.

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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.

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

"StreamSets Transformer is a good feature because it helps you when you are developing applications and when you don't want to write a lot of code. That is the best feature overall."

"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 Ease of configuration for pipes is amazing. It has a lot of connectors. Mainly, we can do everything with the data in the pipe. I really like the graphical interface too"

"StreamSets has definitely helped us in getting the information into our data lake very quickly, in terms of ingestion, and the most important thing is it has helped us from a resourcing point of view because you can easily upskill a BI or ETL resource without any programming knowledge to work with this, which has drastically reduced the time that we are spending on workloads by 60% to 70% as well as reducing the time spent on ingestion by 30%."

"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."

"StreamSets data drift feature gives us an alert upfront so we know that the data can be ingested. Whatever the schema or data type changes, it lands automatically into the data lake without any intervention from us, but then that information is crucial to fix for downstream pipelines, which process the data into models, like Tableau and Power BI models. This is actually very useful for us. We are already seeing benefits. Our pipelines used to break when there were data drift changes, then we needed to spend about a week fixing it. Right now, we are saving one to two weeks. Though, it depends on the complexity of the pipeline, we are definitely seeing a lot of time being saved."

"The best thing about StreamSets is its plugins, which are very useful and work well with almost every data source. It's also easy to use, especially if you're comfortable with SQL. You can customize it to do what you need. Many other tools have started to use features similar to those introduced by StreamSets, like automated workflows that are easy to set up."

"The best feature that I really like is the integration."

More StreamSets pros

"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."

"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

"There aren't enough hands-on labs, and debugging is also an issue because it takes a lot of time. Logs are not that clear when you are debugging, and you can only select a single source for a pipeline."

"Visualization and monitoring need to be improved and refined."

"StreamSet works great for batch processing but we are looking for something that is more real-time. We need latency in numbers below milliseconds."

"We create pipelines or jobs in StreamSets Control Hub. It is a great feature, but if there is a way to have a folder structure or organize the pipelines and jobs in Control Hub, it would be great. I submitted a ticket for this some time back."

"The execution engine could be improved. When I was at their session, they were using some obscure platform to run. There is a controller, which controls what happens on that, but you should be able to easily do this at any of the cloud services, such as Google Cloud. You shouldn't have any issues in terms of how to run it with their online development platform or design platform, basically their execution engine. There are issues with that."

"StreamSets should provide a mechanism to be able to perform data quality assessment when the data is being moved from one source to the target."

"Currently, we can only use the query to read data from SAP HANA. What we would like to see, as soon as possible, is the ability to read from multiple tables from SAP HANA. That would be a really good thing that we could use immediately. For example, if you have 100 tables in SQL Server or Oracle, then you could just point it to the schema or the 100 tables and ingestion information. However, you can't do that in SAP HANA since StreamSets currently is lacking in this. They do not have a multi-table feature for SAP HANA. Therefore, a multi-table origin for SAP HANA would be helpful."

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

More StreamSets 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

"We use the free version. It's great for a public, free release. Our stance is that the paid support model is too expensive to get into. They should honestly reevaluate that."

"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."

"Its pricing is pretty much up to the mark. For smaller enterprises, it could be a big price to pay at the initial stage of operations, but the moment you have the Seed B or Seed C funding and you want to scale up your operations and aren't much worried about the funds, at that point in time, you would need a solution that could be scaled."

"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."

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

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

"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."

"The pricing is affordable for any business."

More StreamSets 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

12%

Manufacturing Company

Insurance Company

Computer Software Company

Real Estate/Law Firm

15%

Manufacturing Company

15%

Retailer

11%

Construction Company

11%

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 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

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Compared 8% of the time

Informatica PowerCenter vs StreamSets

Compared 6% of the time

SSIS vs StreamSets

Compared 5% of the time

Confluent vs StreamSets

Compared 5% of the time

Oracle Data Integrator (ODI) vs StreamSets

Compared 5% of the time

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Databricks vs Upsolver

Compared 11% of the time

Striim vs Upsolver

Compared 10% of the time

SAS Access vs Upsolver

Compared 8% of the time

Alteryx Designer vs Upsolver

Compared 7% of the time

Rivery vs Upsolver

Compared 7% of the time

More Upsolver Competitors

Product Reports

Buyer's Guide

StreamSets

June 2026

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Buyer's Guide

Data Integration

June 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

June 2026

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