Azure Stream Analytics vs Upsolver comparison

Microsoft and Upsolver are both solutions in the Streaming Analytics category. Microsoft is ranked #3 with an average rating of 7.1, while Upsolver is ranked #20 with an average rating of 8.5. Microsoft holds a 6.2% mindshare in SA, compared to Upsolver’s 0.7% mindshare. Additionally, 90% of Microsoft users are willing to recommend the solution, compared to 100% of Upsolver users who would recommend it.

Azure Stream Analytics

Read 30 Azure Stream Analytics reviews

2,877 Views
2,618 Comparison Views

90% willing to recommend

Upsolver

Read 3 Upsolver reviews

612 Views
141 Comparison Views

100% willing to recommend

Azure Stream Analytics

Upsolver

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Executive Summary

We performed a comparison between Azure Stream Analytics and Upsolver based on real PeerSpot user reviews.

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

To learn more, read our detailed Azure Stream Analytics vs. Upsolver Report (Updated: December 2025).

Buyer's Guide

Azure Stream Analytics vs. Upsolver

December 2025

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Helped 881,114 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

Azure Stream Analytics

Ranking in Streaming Analytics

3rd

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Upsolver

Ranking in Streaming Analytics

20th

Average Rating

8.4

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

Data Integration (37th)

Mindshare comparison

As of January 2026, in the Streaming Analytics category, the mindshare of Azure Stream Analytics is 6.2%, down from 11.6% compared to the previous year. The mindshare of Upsolver is 0.7%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Azure Stream Analytics	6.2%
Upsolver	0.7%
Other	93.1%

Streaming Analytics

Featured Reviews

Chandra Mani

Technical architect at Tech Mahindra

Has supported real-time data validation and processing across multiple use cases but can improve consumer-side integration and streamlined customization

I widely use AKS, Azure Kubernetes Service, Azure App Service, and there are APM Gateway kinds of things. I also utilize API Management and Front Door to expose any multi-region application I have, including Web Application Firewalls, and many more—around 20 to 60 services. I use Key Vault for managing secrets and monitoring Azure App Insights for tracing and monitoring. Additionally, I employ AI search for indexer purposes, processing chatbot data or any GenAI integration. I widely use OpenAI for GenAI, integrating various models with our platform. I extensively use hybrid cloud solutions to connect on-premise cloud or cloud to another network, employing public private endpoints or private link service endpoints. Azure DevOps is also on my list, and I leverage many security concepts for end-to-end design. I consider how end users access applications to data storage and secure the entire platform for authenticated users across various use cases, including B2C, B2B, or employee scenarios. I also widely design multi-tenant applications, utilizing Azure AD or Azure AD B2C for consumers. Azure Stream Analytics reads from any real-time stream; it's designed for processing millions of records every millisecond. They utilize Event Hubs for this purpose, as it allows for event processing. After receiving data from various sources, we validate and store it in a data store. Azure Stream Analytics can consume data from Event Hubs, applying basic validation rules to determine the validity of each record before processing.

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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.

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Quotes from Members

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

Pros

"Provides deep integration with other Azure resources."

"Azure Stream Analytics helps with processing fast-moving data in my IoT applications because it's quicker to escalate."

"The life cycle, report management and crash management features are great."

"Azure Stream Analytics reads from any real-time stream; it's designed for processing millions of records every millisecond."

"The integrations for this solution are easy to use and there is flexibility in integrating the tool with Azure Stream Analytics."

"Any time I needed assistance, they were helpful."

"We find the query editor feature of this solution extremely valuable for our business."

"It was easy for me to use from the beginning. I am accustomed to working with Microsoft."

More Azure Stream Analytics pros

"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."

"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."

"It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality."

Cons

"There is a lack of technical support from Microsoft's local office, particularly in Taiwan."

"There are too many products in the Azure landscape, which sometimes leads to overlap between them."

"There is a need for improvement in reprocessing or validation without custom code. Azure Stream Analytics currently allows some degree of code writing, which could be simplified with low-code or no-code platforms to enhance performance."

"The solution’s customer support could be improved."

"Azure Stream Analytics could improve by having clearer metrics as to the scale, more metrics around the data set size that is flowing through it, and performance tuning recommendations."

"The solution offers a free trial, however, it is too short."

"Easier scalability and more detailed job monitoring features would be helpful."

"The initial setup is complex."

More Azure Stream Analytics cons

"There is room for improvement in query tuning."

"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."

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

"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

"When scaling up, the pricing for Azure Stream Analytics can get relatively high. Considering its capabilities compared to other solutions, I would rate it a seven out of ten for cost. However, we've found ways to optimize costs using tools like Databricks for specific tasks."

"Azure Stream Analytics is a little bit expensive."

"The product's price is at par with the other solutions provided by the other cloud service providers in the market."

"I rate the price of Azure Stream Analytics a four out of five."

"We pay approximately $500,000 a year. It's approximately $10,000 a year per license."

"There are different tiers based on retention policies. There are four tiers. The pricing varies based on steaming units and tiers. The standard pricing is $10/hour."

"The licensing for this product is payable on a 'pay as you go' basis. This means that the cost is only based on data volume, and the frequency that the solution is used."

"The cost of this solution is less than competitors such as Amazon or Google Cloud."

More Azure Stream Analytics 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

14%

Computer Software Company

11%

Manufacturing Company

University

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	3
Large Enterprise	18

No data available

Questions from the Community

Which would you choose - Databricks or Azure Stream Analytics?

Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...

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What is your experience regarding pricing and costs for Azure Stream Analytics?

Azure charges in various ways based on incoming and outgoing data processing activities. Choosing between pay-as-you-go or enterprise models can affect pricing, and depending on data volume, charge...

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What needs improvement with Azure Stream Analytics?

There is a need for improvement in reprocessing or validation without custom code. Azure Stream Analytics currently allows some degree of code writing, which could be simplified with low-code or no...

See all answers

What is your experience regarding pricing and costs for Upsolver?

Upsolver is affordable at approximately $225 per terabyte per year. Compared to what I know from others, it's cheaper than many other products.

See all answers

What needs improvement with Upsolver?

There is room for improvement in query tuning. Upsolver could do a more in-depth analysis in employing machine power, such as CPU and memory, to enhance query performance. Furthermore, allocating C...

See all answers

What is your primary use case for Upsolver?

I am working as a consultant and currently have my own consultancy services. I provide services to companies that are data-heavy and looking for data engineering solutions for their business needs....

See all answers

Comparisons

Amazon MSK vs Azure Stream Analytics

Compared 19% of the time

Apache Flink vs Azure Stream Analytics

Compared 15% of the time

Databricks vs Azure Stream Analytics

Compared 15% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 12% of the time

Apache Spark Streaming vs Azure Stream Analytics

Compared 7% of the time

More Azure Stream Analytics Competitors

Databricks vs Upsolver

Compared 28% of the time

Alteryx Designer vs Upsolver

Compared 16% of the time

Rivery vs Upsolver

Compared 16% of the time

Informatica PowerCenter vs Upsolver

Compared 14% of the time

SAS Access vs Upsolver

Compared 13% of the time

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Product Reports

Buyer's Guide

Azure Stream Analytics

January 2026

Download Azure Stream Analytics product report

Buyer's Guide

Data Integration

January 2026

Download Upsolver product report

Also Known As

ASA

No data available

Overview

Azure Stream Analytics is a robust real-time analytics service that has been designed for critical business workloads. Users are able to build an end-to-end serverless streaming pipeline in minutes. Utilizing SQL, users are able to go from zero to production with a few clicks, all easily extensible with unique code and automatic machine learning abilities for the most advanced scenarios.

Azure Stream Analytics has the ability to analyze and accurately process exorbitant volumes of high-speed streaming data from numerous sources at the same time. Patterns and scenarios are quickly identified and information is gathered from various input sources, such as social media feeds, applications, clickstreams, sensors, and devices. These patterns can then be implemented to trigger actions and launch workflows, such as feeding data to a reporting tool, storing data for later use, or creating alerts. Azure Stream Analytics is also offered on Azure IoT Edge runtime, so the data can be processed on IoT devices.

Top Benefits

User friendly: Azure Stream Analytics is very straightforward and easy to use. Out of the box and with a few clicks, users are able to connect to numerous sources and sinks, and easily develop an end-to-end pipeline. Stream Analytics can easily connect to Azure IoT Hub and Azure Event Hub for streaming ingestion, in addition to connecting with Azure Blob storage for historical data ingestion.
Flexible deployment: For low-latency analytics, Azure Stream Analytics can run on Azure Stack or IoT edge. For large-scale analytics, the solution can run in the cloud. Azure Stream Analytics uses the same query language and tools for both the cloud and the edge, facilitating an easier process for developers to design exceptional hybrid architectures for streaming processes.
Cost-effective: With Azure Stream Analytics, users only pay for the streaming units they consume; there are no upfront costs. Users can easily scale up or down as needed; there is no commitment or cluster provisioning.
Trustworthy: Azure Stream Analytics guarantees event processing to be 99.99% available with a minute level of granularity. Azure Stream Analytics has embedded recovery capabilities and checkpoints to keep things running smoothly at all times. Events are never lost with Azure Stream Analytics at-least once delivery of events and exactly one event processing.

Reviews from Real Users

“Azure Stream Analytics is something that you can use to test out streaming scenarios very quickly in the general sense and it is useful for IoT scenarios. If I was to do a project with IoT and I needed a streaming solution, Azure Stream Analytics would be a top choice. The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex.” - Olubisi A., Team Lead at a tech services company.

“It's used primarily for data and mining - everything from the telemetry data side of things. It's great for streaming and makes everything easy to handle. The streaming from the IoT hub and the messaging are aspects I like a lot.” - Sudhendra U., Technical Architect at Infosys

Microsoft

Upsolver's mission is to turn engineering-intensive data lakes into easy-to-use repositories that serve 50X more data professionals compared to the status-quo today. Upsolver cuts 95% from the tedious process of preparing event data for analytics and machine learning, using a visual SQL-based interface, a cloud-native data platform and deep technology for stream-processing, indexing and eventually-consistent file systems.

Upsolver

Sample Customers

Rockwell Automation, Milliman, Honeywell Building Solutions, Arcoflex Automation Solutions, Real Madrid C.F., Aerocrine, Ziosk, Tacoma Public Schools, P97 Networks

Information Not Available

Buyer's Guide

Azure Stream Analytics vs. Upsolver

December 2025

Free Report: Azure Stream Analytics vs. Upsolver

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