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Amazon Kinesis vs Upsolver comparison

 

Comparison Buyer's Guide

Executive Summary

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

Amazon Kinesis
Ranking in Streaming Analytics
2nd
Average Rating
8.0
Reviews Sentiment
7.0
Number of Reviews
29
Ranking in other categories
No ranking in other categories
Upsolver
Ranking in Streaming Analytics
21st
Average Rating
8.4
Reviews Sentiment
7.4
Number of Reviews
3
Ranking in other categories
Data Integration (39th)
 

Mindshare comparison

As of March 2026, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 5.2%, down from 9.0% compared to the previous year. The mindshare of Upsolver is 0.9%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics Mindshare Distribution
ProductMindshare (%)
Amazon Kinesis5.2%
Upsolver0.9%
Other93.9%
Streaming Analytics
 

Featured Reviews

CD
AWS Cloud Architect at a healthcare company with 10,001+ employees
Real-time streaming and seamless integration enhance workloads with room for competitive pricing improvements
Amazon Kinesis is easy to get started with, provides good documentation, and has a multilang daemon interface that makes it programming-language agnostic. The throughput is convenient for processing volumes out of the box and does not require complex configurations. It also provides auto-scaling with different partition keys into various shards. Lambda's scalability, seamless integration with other AWS services, and support for multiple programming languages are very beneficial.
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

"Amazon Kinesis has improved our ROI."
"We have seen a return on our investment with Amazon Kinesis, as we are able to process data without any issue and it is our solution for ingesting data in other databases, such as Snowflake."
"The product's initial setup phase is not difficult because we are using the tool on the cloud."
"Setting Amazon Kinesis up is quick and easy; it only takes a few minutes to configure the necessary settings and start using it."
"The scalability is pretty good, as one can have any number of nodes spawned or replicated on the primary so that any load can be handled, perhaps a few terabytes with ease in around 15 seconds."
"One of the best features of Amazon Kinesis is the multi-partition."
"The solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."
"We have been able to drop our costs for ingesting data by about 60 to 70%."
"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."
"Customer service is excellent, and I would rate it between eight point five to nine out of ten."
"It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality."
 

Cons

"One area for improvement in the solution is the file size limitation of 10 Mb. My company works with files with a larger file size. The batch size and throughput also need improvement in Amazon Kinesis."
"If there were better documentation on optimal sharding strategies then it would be helpful."
"Amazon Kinesis involved a more complex setup and configuration than Azure Event Hub."
"I suggest integrating additional features, such as incorporating Amazon Pinpoint or Amazon Connect as bundled offerings, rather than deploying them as separate services."
"Amazon Kinesis should improve its limits."
"In general, the pain point for us was that once the data gets into Kinesis there is no way for us to understand what's happening because Kinesis divides everything into shards. So if we wanted to understand what's happening with a particular shard, whether it is published or not, we could not. Even with the logs, if we want to have some kind of logging it is in the shard."
"We were charged high costs for the solution’s enhanced fan-out feature."
"In general, the pain point for us was that once the data gets into Kinesis there is no way for us to understand what's happening because Kinesis divides everything into shards."
"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."
"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

"The tool's pricing is cheap."
"Under $1,000 per month."
"The solution's pricing is fair."
"The pricing depends on the use cases and the level of usage. If you wanted to use Kinesis for different use cases, there's definitely a cheaper base cost involved. However, it's not entirely cheap, as different use cases might require different levels of Kinesis usage."
"I rate the product price a five on a scale of one to ten, where one is cheap, and ten is expensive."
"Amazon Kinesis pricing is sometimes reasonable and sometimes could be better, depending on the planning, so it's a five out of ten for me."
"The product falls on a bit of an expensive side."
"Amazon Kinesis is an expensive solution."
"Upsolver is affordable at approximately $225 per terabyte per year."
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Top Industries

By visitors reading reviews
Computer Software Company
15%
Financial Services Firm
13%
Manufacturing Company
6%
Construction Company
5%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business8
Midsize Enterprise10
Large Enterprise9
No data available
 

Questions from the Community

What do you like most about Amazon Kinesis?
Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive.
What is your experience regarding pricing and costs for Amazon Kinesis?
Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.
What needs improvement with Amazon Kinesis?
We are contemplating moving away from Amazon Kinesis primarily because of the cost. It is very useful, but if we write our own analytics and data processing pipeline, it would be much cheaper for u...
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

Amazon AWS Kinesis, AWS Kinesis, Kinesis
No data available
 

Overview

 

Sample Customers

Zillow, Netflix, Sonos
Information Not Available
Find out what your peers are saying about Amazon Kinesis vs. Upsolver and other solutions. Updated: March 2026.
885,311 professionals have used our research since 2012.