Azure Stream Analytics vs Striim comparison

Microsoft and Striim are both solutions in the Streaming Analytics category. Microsoft is ranked #2 with an average rating of 6.7, while Striim is ranked #23 with an average rating of 8.0. Microsoft holds a 6.6% mindshare in SA, compared to Striim’s 1.7% mindshare. Additionally, 90% of Microsoft users are willing to recommend the solution, compared to 100% of Striim users who would recommend it.

Azure Stream Analytics

Read 30 Azure Stream Analytics reviews

6,312 Views
5,870 Comparison Views

90% willing to recommend

Striim

Read 2 Striim reviews

1,932 Views
638 Comparison Views

100% willing to recommend

Azure Stream Analytics

Striim

Comparison Buyer's Guide

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Executive Summary

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

Find out what your peers are saying about Databricks, Microsoft, Apache and others in Streaming Analytics.

To learn more, read our detailed Streaming Analytics Report (Updated: July 2026).

Buyer's Guide

Streaming Analytics

July 2026

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Helped 909,153 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

2nd

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Striim

Ranking in Streaming Analytics

23rd

Average Rating

8.0

Reviews Sentiment

6.2

Number of Reviews

Ranking in other categories

Data Integration (47th), Cloud Data Integration (27th)

Mindshare comparison

As of August 2026, in the Streaming Analytics category, the mindshare of Azure Stream Analytics is 6.6%, down from 8.7% compared to the previous year. The mindshare of Striim is 1.7%, up from 0.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Azure Stream Analytics	6.6%
Striim	1.7%
Other	91.7%

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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Ramakanth Vanga

Data Engineer

Real-time data capture has accelerated releases and now improves trust in our data warehouse

The checkpoints would help me to figure out where the problem was if there's any lag, but I had to do a lot of manual work to figure out where the lag is. Striim would not intuitively tell me the culprit table or database behind the lag. I believe that is an improvement Striim could definitely do. Passwords were an issue. Property variables were not supported for passwords, meaning I had to make sure the password is manually populated. I believe if Striim could read from AWS secrets or its own secret mechanism to store the password, that would really save a lot of time so that I don't have to keep updating the password whenever there is any change. The user experience of triggering alerts if there's any lag which Striim identified, which is outside normal processing time, could intrinsically be done by Striim. I believe that was lacking. I would wait for Striim to tell me, instead of me going and validating whether Striim is lagging behind. If Striim could itself tell me that it's seeing a lot more volume than expected, that would really make me give it a higher number.

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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

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

"It has features and functionality to integrate with all the tools that are available in the market, not only Azure solutions."

"The way it organizes data into tables and dashboards is very helpful."

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

"It's easy to implement and maintain pipelines with minimal complexity."

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

"Technical support is pretty helpful."

"If you're not doing terribly complex scenarios, this is a quick and fast way to have your streaming pipeline set up."

More Azure Stream Analytics pros

"Striim is capable of absorbing a large number of transactions, and the difference between the two databases is always less than a second, which demonstrates efficiency and highlights the variety of sources and targets I have used."

"We were confidently in a situation to call Snowflake as a single source of truth, and I believe with Striim, we were able to do that because without Striim, the SLA would be much higher, and there would not have been much confidence in Snowflake."

Cons

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

"We had some issues with the jobs not behaving properly. They promise a lot, however, sometimes that doesn't happen and we realized that later."

"Early in the process, we had some issues with stability."

"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."

"One area that could use improvement is the handling of data validation. Currently, there is a review process, but sometimes the validation fails even before the job is executed. This results in wasted time as we have to rerun the job to identify the failure."

"The only challenge was that the streaming analytics area in Azure Stream Analytics could not meet our company's expectations, making it a component where improvements are required."

"The pricing is a little bit high."

"There may be some issues when connecting with Microsoft Power BI because we are providing the input and output commands, and there's a chance of it being delayed while connecting."

More Azure Stream Analytics cons

"I think Striim could be improved with better pricing and enhanced documentation."

"The user experience of triggering alerts if there's any lag which Striim identified, which is not normal, could intrinsically be done by Striim."

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."

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

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

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

"Azure Stream Analytics is a little bit expensive."

"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."

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

More Azure Stream Analytics pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

12%

Manufacturing Company

Computer Software Company

Comms Service Provider

Construction Company

16%

Healthcare Company

13%

Retailer

12%

Financial Services Firm

10%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	3
Large Enterprise	17

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...

See all answers

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...

See all answers

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 Striim?

My experience with the pricing, implementation cost, and licensing of Striim is that it is somewhat expensive.

See all answers

What needs improvement with Striim?

I think Striim could be improved with better pricing and enhanced documentation.

See all answers

What is your primary use case for Striim?

I use Striim to perform change data capture from relational databases to non-relational databases in my organization. I implement CDC with Striim by transferring data from Oracle Database to MongoD...

See all answers

Comparisons

Databricks vs Azure Stream Analytics

Compared 12% of the time

Confluent vs Azure Stream Analytics

Compared 9% of the time

Apache Spark Streaming vs Azure Stream Analytics

Compared 9% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 8% of the time

Apache Flink vs Azure Stream Analytics

Compared 8% of the time

More Azure Stream Analytics Competitors

Qlik Talend Cloud vs Striim

Compared 14% of the time

Upsolver vs Striim

Compared 10% of the time

Oracle GoldenGate vs Striim

Compared 10% of the time

AWS Database Migration Service vs Striim

Compared 10% of the time

Confluent vs Striim

Compared 9% of the time

More Striim Competitors

Product Reports

Buyer's Guide

Azure Stream Analytics

August 2026

Download Azure Stream Analytics product report

Buyer's Guide

Streaming Analytics

July 2026

Download Striim product report

Also Known As

ASA

Striim Platform

Overview

Azure Stream Analytics offers real-time data processing with seamless IoT hub integration and user-friendly setup. It efficiently manages data streams and supports Azure services, SQL Server, and Cosmos DB.

Azure Stream Analytics specializes in real-time data analytics, easily integrating with Microsoft technologies. It enables swift deployment, monitoring, and high-performance data streaming. Though praised for its powerful SQL language and machine learning capabilities, users face challenges with historical analysis, pricing clarity, debugging, and data connection outside Azure. Limited real-time data joining, query customization, and complex data handling are noted alongside needs for improved technical support, job monitoring, and trial periods.

What are the key features of Azure Stream Analytics?

IoT Hub Integration: Seamless connection with IoT devices for efficient data stream management.
Real-Time Analytics: Capable of processing and analyzing vast data volumes instantly.
SQL-Based Language: Powerful language for simple and flexible query creation.
Azure Service Integration: Compatible with Azure Storage, SQL Server, Cosmos DB.
Interface: User-friendly setup and swift deployment for efficient monitoring.
Machine Learning: Enhanced capabilities support predictive and preventive data analysis.

What benefits or ROI should users consider?

High-Performance Streaming: Efficient throughput for real-time data applications.
Data Partitioning: Supports scalability and flexibility in processing large data sets.
Minimal Setup Requirements: Quick and easy deployment, reducing initial setup time.
Integration Ease: Compatibility with Microsoft technologies simplifies implementation.

Azure Stream Analytics is leveraged in industries for real-time IoT data processing, predictive analytics, and accident prevention in logistics. It supports telemetry data processing for applications like predictive maintenance and integrates with Power BI for enhanced data visualization, aligning with Azure's IoT infrastructure.

Microsoft

Striim offers a comprehensive platform for real-time data integration and streaming analytics, designed to streamline data processes for enterprise-level solutions.

Striim enables seamless migration and integration of data across cloud and on-premises environments, making it ideal for businesses looking to leverage real-time analytics. Its capabilities support continuous data flow, reducing latency and enhancing decision-making. Designed for scalable and secure data management, Striim facilitates effective data-driven strategies.

What are some key features of Striim?

Continuous Data Ingestion: Collects and processes real-time data streams promptly.
Data Integration: Combines data from different sources for unified insights.
Real-Time Analytics: Supports instant analytics, allowing fast response to changes.
Scalable Architecture: Adapts to large-scale data environments efficiently.

What benefits should users look for in Striim reviews?

Improved Data Accuracy: Ensures data integrity in analytics.
Enhanced Operational Efficiency: Streamlined processes for faster data handling.
Cost-Effective Integration: Reduces expenses associated with traditional data handling.
Flexible Deployment Options: Allows for tailored solutions fitting specific needs.

In industries like finance, Striim supports real-time fraud detection by providing uninterrupted data streaming between transaction systems. In healthcare, it enables rapid data processing for patient monitoring, improving service delivery. Manufacturing uses Striim to enhance supply chain visibility through real-time data analytics.

Striim

Sample Customers

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

Sky, UPS, MACY'S, EMAAR, HSBC

Find out what your peers are saying about Databricks, Microsoft, Apache and others in Streaming Analytics. Updated: July 2026.

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