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Skyvia vs Upsolver comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Mar 1, 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

Skyvia
Ranking in Data Integration
56th
Average Rating
9.0
Reviews Sentiment
7.8
Number of Reviews
1
Ranking in other categories
Cloud Data Integration (26th)
Upsolver
Ranking in Data Integration
38th
Average Rating
8.4
Reviews Sentiment
7.4
Number of Reviews
3
Ranking in other categories
Streaming Analytics (21st)
 

Mindshare comparison

As of April 2026, in the Data Integration category, the mindshare of Skyvia is 0.8%, up from 0.3% 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
ProductMindshare (%)
Upsolver0.7%
Skyvia0.8%
Other98.5%
Data Integration
 

Featured Reviews

RH
CTO & Developer at a consultancy with self employed
The product works, is simple to use, and is reliable.
Error handling. This has caused me many problems in the past. When an error occurs, the event on the connection that is called does not seem to behave as documented. If I attempt a retry or opt not to display an error dialog, it does it anyway. In all fairness, I have never reported this. I think it is more important that a unique error code is passed to the error event that identifies a uniform type of error that occurred, such as ecDisconnect, eoInvalidField. It is very hard to find what any of the error codes currently passed actually mean. A list would be great for each database engine. Trying to catch an exception without displaying the UniDAC error message is impossible, no matter how you modify the parameters in the OnError of the TUniConnection object. I have already implemented the following things myself. They are suggestions rather than specific requests. Copy Datasets: This contains an abundance of redundant options. I think that a facility to copy one dataset to another in a single call would be handy. Redundancy: I am currently working on this. I have extended the TUniConnection to have an additional property called FallbackConnection. If the TUniConnection goes offline, the connection attempts to connect the FallbackConnection. If successful, it then sets the Connection properties of all live UniDatasets in the app to the FallbackConnection and re-opens them if necessary. The extended TUniConnection holds a list of datasets that were created. Each dataset is responsible for registering itself with the connection. This is a highly specific feature. It supports an offline mode that is found in mission critical/point of sale solutions. I have never seen it implement before in any DACs, but I think it is a really unique feature with a big impact. Dataset to JSON/XML: A ToSql function on a dataset that creates a full SQL Text statement with all parameters converted to text (excluding blobs) and included in the returned string. Extended TUniScript:- TMyUniScript allows me to add lines of text to a script using the normal dataset functions, Script.Append, Script.FieldByName(‘xxx’).AsString := ‘yyy’, Script.AddToScript and finally Script.Post, then Script.Commit. The AddToScript builds the SQL text statement and appends it to the script using #e above. Record Size Calculation. It would be great if UniDac could estimate the size of a particular record from a query or table. This could be used to automatically set the packet fetch/request count based on the size of the Ethernet packets on the local area network. This I believe would increase performance and reduce network traffic for returning larger datasets. I am aware that this would also be a unique feature to UniDac but would gain a massive performance enhancement. I would suggest setting the packet size on the TUniConnection which would effect all linked datasets.
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

"For what it offers, I think this solution is a must for any Delphi programmer."
"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

"Error handling has caused me many problems in the past; when an error occurs, the event on the connection that is called does not seem to behave as documented."
"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."
"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."
 

Pricing and Cost Advice

Information not available
"Upsolver is affordable at approximately $225 per terabyte per year."
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Top Industries

By visitors reading reviews
Performing Arts
20%
Construction Company
11%
Outsourcing Company
8%
Computer Software Company
7%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
No data available
 

Questions from the Community

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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...
 

Comparisons

 

Also Known As

Skyvia, Skyvia Data Integration
No data available
 

Overview

 

Sample Customers

Boeing, Sony, Honda, Oracle, BMW, Samsung
Information Not Available
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