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Matillion Data Productivity Cloud vs Upsolver comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Jan 18, 2026

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

Matillion Data Productivity...
Average Rating
8.4
Reviews Sentiment
7.4
Number of Reviews
28
Ranking in other categories
Cloud Data Integration (11th), AI Data Analysis (14th)
Upsolver
Average Rating
8.4
Reviews Sentiment
7.4
Number of Reviews
3
Ranking in other categories
Data Integration (37th), Streaming Analytics (21st)
 

Mindshare comparison

While both are Data Integration and Access solutions, they serve different purposes. Matillion Data Productivity Cloud is designed for Cloud Data Integration and holds a mindshare of 5.7%, up 3.2% compared to last year.
Upsolver, on the other hand, focuses on Data Integration, holds 0.7% mindshare, up 0.1% since last year.
Cloud Data Integration Mindshare Distribution
ProductMindshare (%)
Matillion Data Productivity Cloud5.7%
AWS Glue8.2%
AWS Database Migration Service6.7%
Other79.4%
Cloud Data Integration
Data Integration Mindshare Distribution
ProductMindshare (%)
Upsolver0.7%
Informatica Intelligent Data Management Cloud (IDMC)3.5%
SSIS3.5%
Other92.3%
Data Integration
 

Featured Reviews

Jitendra Jena - PeerSpot reviewer
Director Axtria - Ingenious Insights! at Axtria - Ingenious Insights
Easy integration and workflow proposals streamline processes
The predefined connectors eliminate the need to write code for connectivity. If you have a predefined connector, it is easy to use with plug and play functionality. The processing time and ease of use are significant benefits. As everyone is moving into AI integration, it will definitely help. When creating workflows, they can propose solutions directly.
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

"It's highly scalable. It takes upon itself the Redshift scalability, so it's very good."
"The technical support treats us well. They already have a support portal, and they are responsive, which helps."
"We allow non-technical people to use Matillion to load data into our data warehouse for reporting, so it is easy enough to use that we don't always have to get a technical person involved in setting up a data movement (ETL)."
"It is an incredibly user-friendly and intuitive tool, making the learning curve quite smooth"
"The most valuable feature of Matillion ETL is the UI experience in which you can drag and drop most of the transformation."
"Matillion's technical support is excellent."
"The solution's most valuable feature is the CDC (Change Data Capture) component."
"It has good integrations with Amazon Redshift and other AWS services."
"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."
"Customer service is excellent, and I would rate it between eight point five to nine out of ten."
 

Cons

"Its integration with SAP connection is not so nice, which should be improved."
"In the next release, we would like to have connections to more databases."
"Matillion’s on-premises capabilities don’t allow you to build something customized."
"In the next release, we would like to have connections to more databases."
"The product's scalability needs improvement. Perhaps adding more connectors would be beneficial."
"I am looking forward to seeing the expansion of the source range for their data loader product."
"I found some of the more complex aspects of ETL challenging, but I grasped the concepts fairly quickly."
"Our main challenge currently is that Matillion runs on an EC2 instance, limiting us to running only two processes simultaneously at the entry level."
"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 product must improve its pricing."
"It is not necessarily a cheap solution. However, it's reasonable priced, especially with the smaller machines that we run it on."
"Matillion ETL has a pay-as-you-go pricing model of a few dollars per hour of runtime."
"Purchasing it through the AWS Marketplace is pretty convenient. There is a little bit of back and forth in terms of the licensing based on the machine size, but it seems to have worked out well. it is convenient to have it all as part of our AWS billing."
"The pricing depends on what edition the customer opts for. For example, the standard edition is priced at $2.00 per credit. And you are only charged when you use it. You're not charged when it's idle."
"The absence of licensing commitments makes it easy to experiment with the tool, and if we decide it's not suitable, we can simply stop the ETL instance and cease incurring charges."
"The AWS pricing and licensing are a cost-effective solution for data integration needs."
"The cost of the solution is high and could be reduced."
"Upsolver is affordable at approximately $225 per terabyte per year."
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
10%
Manufacturing Company
9%
Construction Company
6%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business6
Midsize Enterprise10
Large Enterprise11
No data available
 

Questions from the Community

What do you like most about Matillion ETL?
The new version with the Productivity Cloud is very simple. It's easy to use, navigate, and understand.
What is your experience regarding pricing and costs for Matillion ETL?
The pricing is managed by the tooling team. The pricing is moderate, neither expensive nor cheap.
What needs improvement with Matillion ETL?
The main areas for improvement are AI features and scalability.
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, ...
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...
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...
 

Also Known As

Matillion ETL for Redshift, Matillion ETL for Snowflake, Matillion ETL for BigQuery
No data available
 

Overview

 

Sample Customers

Thrive Market, MarketBot, PWC, Axtria, Field Nation, GE, Superdry, Quantcast, Lightbox, EDF Energy, Finn Air, IPRO, Twist, Penn National Gaming Inc
Information Not Available
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