Azure Stream Analytics vs Spring Cloud Data Flow comparison

Microsoft and VMware are both solutions in the Streaming Analytics category. Microsoft is ranked #4 with an average rating of 7.1, while VMware is ranked #9 with an average rating of 7.8. Microsoft holds a 8.8% mindshare in SA, compared to VMware’s 4.7% mindshare. Additionally, 89% of Microsoft users are willing to recommend the solution, compared to 88% of VMware users who would recommend it.

Azure Stream Analytics

Read 28 Azure Stream Analytics reviews

3,832 Views
3,487 Comparison Views

89% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

3,216 Views
1,544 Comparison Views

88% willing to recommend

Azure Stream Analytics

Spring Cloud Data Flow

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 17, 2024

Azure Stream Analytics and Spring Cloud Data Flow compete in the data stream management category. Azure Stream Analytics has a competitive edge due to its easy integration with other Azure services and real-time analytics capabilities.

Features: Azure Stream Analytics integrates seamlessly with Azure environments, offering real-time analytics and ease of use with a SQL-based approach. It supports IoT Hub and Blob Storage for comprehensive data handling. Spring Cloud Data Flow provides flexibility with plug-and-play features, supporting workflow automation and effective microservices orchestration.

Room for Improvement: Azure Stream Analytics could be improved by enhancing cross-cloud interoperability and pricing transparency. Users find challenges with real-time joins and integration outside Azure. Spring Cloud Data Flow would benefit from better documentation and user interface improvements, with its open-source aspect sometimes challenging due to limited community support.

Ease of Deployment and Customer Service: Azure Stream Analytics offers strong technical support from Microsoft with timely assistance and extensive documentation, commonly used in public cloud environments. Spring Cloud Data Flow, typically deployed on-premises or private clouds, relies on community forums for support and is less directly backed compared to Azure's extensive services.

Pricing and ROI: Azure Stream Analytics is known for competitive cloud solution pricing with models such as pay-as-you-go, but faces criticism regarding pricing transparency and costs when scaling. Spring Cloud Data Flow offers a cost-efficient open-source model, with paid support required for enhanced services. Both deliver solid ROI, with Azure praised for quick setup and Spring Cloud Data Flow for value via its community edition.

To learn more, read our detailed Azure Stream Analytics vs. Spring Cloud Data Flow Report (Updated: July 2025).

Buyer's Guide

Azure Stream Analytics vs. Spring Cloud Data Flow

July 2025

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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

4th

Average Rating

7.8

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Spring Cloud Data Flow

Ranking in Streaming Analytics

9th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (20th)

Mindshare comparison

As of August 2025, in the Streaming Analytics category, the mindshare of Azure Stream Analytics is 8.8%, down from 12.5% compared to the previous year. The mindshare of Spring Cloud Data Flow is 4.7%, up from 4.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

SantiagoCordero

Director, Governance & Infrastructure & Director at VASS

Native connectors and integration simplify tasks but portfolio complexity needs addressing

There are too many products in the Azure landscape, which sometimes leads to overlap between them. Microsoft continuously releases new products or solutions, which can be frustrating when determining the appropriate features from one solution over another. A cost comparison between products is also not straightforward. They should simplify their portfolio. The Microsoft licensing system is confusing and not easy to understand, and this is something they should address. In the future, I may stop using Stream Analytics and move to other solutions. I discussed Palantir earlier, which is something I want to explore in depth because it allows me to accomplish more efficiently compared to solely using Azure. Additionally, the vendors should make the solution more user-friendly, incorporating low-code and no-code features. This is something I wish to explore further.

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NitinGoyal

Engineering Lead at Naukri.com

Has a plug-and-play model and provides good robustness and scalability

The solution's community support could be improved. I don't know why the Spring Cloud Data Flow community is not very strong. Community support is very limited whenever you face any problem or are stuck somewhere. I'm not sure whether it has improved in the last six months because this pipeline was set up almost two years ago. I struggled with that a lot. For example, there was limited support whenever I got an exception and sought help from Stack Overflow or different forums. Interacting with Kubernetes needs a few certificates. You need to define all the certificates within your application. With the help of those certificates, your Java application or Spring Cloud Data Flow can interact with Kubernetes. I faced a lot of hurdles while placing those certificates. Despite following the official documentation to define all the replicas, readiness, and liveliness probes within the Spring Cloud Data Flow application, it was not working. So, I had to troubleshoot while digging in and debugging the internals of Spring Cloud Data Flow at that time. It was just a configuration mismatch, and I was doing nothing weird. There was a small spelling difference between how Spring Cloud Data Flow was expecting it and how I passed it. I was just following the official documentation.

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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

"It provides the capability to streamline multiple output components."

"The best features of Azure Stream Analytics are that it's easy to set up and configure."

"Technical support is pretty helpful."

"Provides deep integration with other Azure resources."

"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."

"It's a product that can scale."

"The solution's technical support is good."

More Azure Stream Analytics pros

"The dashboards in Spring Cloud Dataflow are quite valuable."

"The most valuable features of Spring Cloud Data Flow are the simple programming model, integration, dependency Injection, and ability to do any injection. Additionally, auto-configuration is another important feature because we don't have to configure the database and or set up the boilerplate in the database in every project. The composability is good, we can create small workloads and compose them in any way we like."

"The best thing I like about Spring Cloud Data Flow is its plug-and-play model."

"The product is very user-friendly."

"There are a lot of options in Spring Cloud. It's flexible in terms of how we can use it. It's a full infrastructure."

"The ease of deployment on Kubernetes, the seamless integration for orchestration of various pipelines, and the visual dashboard that simplifies operations even for non-specialists such as quality analysts."

"The most valuable feature is real-time streaming."

"The solution's most valuable feature is that it allows us to use different batch data sources, retrieve the data, and then do the data processing, after which we can convert and store it in the target."

Cons

"The UI should be a little bit better from a usability perspective."

"The solution's interface could be simpler to understand for non-technical people."

"The initial setup is complex."

"Azure Stream Analytics is challenging to customize because it's not very flexible."

"I would like to have a contact individual at Microsoft."

"It is not complex, but it requires some development skills. When the data is sent from Azure Stream Analytics to Power BI, I don't have the access to modify the data. I can't customize or edit the data or do some queries. All queries need to be done in the Azure Stream Analytics."

"We would like to have centralized platform altogether since we have different kind of options for data ingestion. Sometimes it gets difficult to manage different platforms."

"The collection and analysis of historical data could be better."

More Azure Stream Analytics cons

"Spring Cloud Data Flow could improve the user interface. We can drag and drop in the application for the configuration and settings, and deploy it right from the UI, without having to run a CI/CD pipeline. However, that does not work with Kubernetes, it only works when we are working with jars as the Spring Cloud Data Flow applications."

"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required."

"The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."

"I would improve the dashboard features as they are not very user-friendly."

"There were instances of deployment pipelines getting stuck, and the dashboard not always accurately showing the application status, requiring manual intervention such as rerunning applications or refreshing the dashboard."

"Some of the features, like the monitoring tools, are not very mature and are still evolving."

"The solution's community support could be improved."

"Spring Cloud Data Flow is not an easy-to-use tool, so improvements are required."

Pricing and Cost Advice

"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."

"Azure Stream Analytics is a little bit expensive."

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

"The current price is substantial."

"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."

"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."

More Azure Stream Analytics pricing and cost advice

"This is an open-source product that can be used free of charge."

"The solution provides value for money, and we are currently using its community edition."

"If you want support from Spring Cloud Data Flow there is a fee. The Spring Framework is open-source and this is a free solution."

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

14%

Manufacturing Company

Retailer

Financial Services Firm

27%

Computer Software Company

16%

Retailer

Insurance Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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?

The solution does not need any license; it comes with your subscription.

See all answers

What needs improvement with Azure Stream Analytics?

It does not always give you the right reason or the correct reason. For example, if a service is stopped, it just tells you that it stopped and started. It does not give you any good insight as to ...

See all answers

What needs improvement with Spring Cloud Data Flow?

There were instances of deployment pipelines getting stuck, and the dashboard not always accurately showing the application status, requiring manual intervention such as rerunning applications or r...

See all answers

What is your primary use case for Spring Cloud Data Flow?

We had a project for content management, which involved multiple applications each handling content ingestion, transformation, enrichment, and storage for different customers independently. We want...

See all answers

What advice do you have for others considering Spring Cloud Data Flow?

I would definitely recommend Spring Cloud Data Flow. It requires minimal additional effort or time to understand how it works, and even non-specialists can use it effectively with its friendly docu...

See all answers

Comparisons

Amazon MSK vs Azure Stream Analytics

Compared 22% of the time

Databricks vs Azure Stream Analytics

Compared 22% of the time

Apache Flink vs Azure Stream Analytics

Compared 16% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 16% of the time

Apache Spark Streaming vs Azure Stream Analytics

Compared 4% of the time

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Apache Flink vs Spring Cloud Data Flow

Compared 38% of the time

TIBCO BusinessWorks vs Spring Cloud Data Flow

Compared 10% of the time

Apache Spark Streaming vs Spring Cloud Data Flow

Compared 6% of the time

MuleSoft Anypoint Platform vs Spring Cloud Data Flow

Compared 6% of the time

Google Cloud Dataflow vs Spring Cloud Data Flow

Compared 5% of the time

More Spring Cloud Data Flow Competitors

Product Reports

Buyer's Guide

Azure Stream Analytics

August 2025

Download Azure Stream Analytics product report

Buyer's Guide

Spring Cloud Data Flow

July 2025

Download Spring Cloud Data Flow 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

Spring Cloud Data Flow is a toolkit for building data integration and real-time data processing pipelines.
Pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. This makes Spring Cloud Data Flow suitable for a range of data processing use cases, from import/export to event streaming and predictive analytics. Use Spring Cloud Data Flow to connect your Enterprise to the Internet of Anything—mobile devices, sensors, wearables, automobiles, and more.

VMware

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. Spring Cloud Data Flow

July 2025

Free Report: Azure Stream Analytics vs. Spring Cloud Data Flow

Find out what your peers are saying about Azure Stream Analytics vs. Spring Cloud Data Flow and other solutions. Updated: July 2025.

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