Azure Stream Analytics vs Spring Cloud Data Flow comparison

Azure Stream Analytics vs. Spring Cloud Data Flow

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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

3rd

Average Rating

8.0

Reviews Sentiment

6.9

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 (24th)

Mindshare comparison

As of May 2025, in the Streaming Analytics category, the mindshare of Azure Stream Analytics is 9.8%, down from 12.6% compared to the previous year. The mindshare of Spring Cloud Data Flow is 4.9%, 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

"The solution has a lot of functionality that can be pushed out to companies."

"Provides deep integration with other Azure resources."

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

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

"The solution's technical support is good."

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

"It's scalable as a cloud product."

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

More Azure Stream Analytics pros

"The product is very user-friendly."

"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 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."

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

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

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

Cons

"The solution doesn't handle large data packets very efficiently, which could be improved upon."

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

"Its features for event imports and architecture could be enhanced."

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

"More flexibility in terms of writing queries and accommodating additional facilities would be beneficial."

"The solution could be improved by providing better graphics and including support for UI and UX testing."

"Sometimes when we connect Power BI, there is a delay or it throws up some errors, so we're not sure."

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

More Azure Stream Analytics cons

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

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

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

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

"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."

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

"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."

Pricing and Cost Advice

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

"The current price is substantial."

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

"Azure Stream Analytics is a little bit expensive."

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

"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."

"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."

"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."

More Azure Stream Analytics pricing and cost advice

"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."

"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."

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850,028 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Computer Software Company

15%

Financial Services Firm

14%

Manufacturing Company

10%

Retailer

Financial Services Firm

26%

Computer Software Company

18%

Retailer

Healthcare 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...

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

I have no problem with pricing. We sell the data analytics value and operational value to customers, focusing on productivity and efficiency from the cloud, rather than just the infrastructure or p...

What needs improvement with Azure Stream Analytics?

There is a lack of technical support from Microsoft's local office, particularly in Taiwan. We often have to learn online, and language can be a communication barrier since not many IT staff can sp...

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...

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...

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...

Databricks vs Azure Stream Analytics

Comparisons

Compared 26% of the time

Amazon MSK vs Azure Stream Analytics

Compared 24% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 19% of the time

Apache Flink vs Azure Stream Analytics

Compared 16% of the time

Confluent vs Azure Stream Analytics

Compared 4% of the time

More Azure Stream Analytics Competitors

Apache Flink vs Spring Cloud Data Flow

Compared 42% of the time

TIBCO BusinessWorks vs Spring Cloud Data Flow

Compared 11% of the time

Google Cloud Dataflow vs Spring Cloud Data Flow

Compared 7% of the time

Apache Spark Streaming vs Spring Cloud Data Flow

Compared 5% of the time

MuleSoft Anypoint Platform vs Spring Cloud Data Flow

Compared 5% of the time

More Spring Cloud Data Flow Competitors

Product Reports

Azure Stream Analytics

Download Azure Stream Analytics product report

Spring Cloud Data Flow

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

Azure Stream Analytics vs. Spring Cloud Data Flow