Azure Stream Analytics vs Spring Cloud Data Flow comparison

Microsoft and Broadcom are both solutions in the Streaming Analytics category. Microsoft is ranked #2 with an average rating of 6.7, while Broadcom is ranked #17 with an average rating of 7.5. Microsoft holds a 6.6% mindshare in SA, compared to Broadcom’s 2.6% mindshare. Additionally, 90% of Microsoft users are willing to recommend the solution, compared to 88% of Broadcom 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

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

4,388 Views
1,448 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: August 2026).

Buyer's Guide

Azure Stream Analytics vs. Spring Cloud Data Flow

August 2026

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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

2nd

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Spring Cloud Data Flow

Ranking in Streaming Analytics

17th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (31st)

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 Spring Cloud Data Flow is 2.6%, down from 4.7% 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%
Spring Cloud Data Flow	2.6%
Other	90.8%

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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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.

Read full review

Quotes from Members

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

Pros

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

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

"Real-time analytics is the most valuable feature of this solution. I can send the collected data to Power BI in real time."

"The product is very user-friendly."

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

"It's a product that can scale."

"The most valuable features are the IoT hub and the Blob storage."

"The solution's most valuable feature is its ability to create a query using SQ."

More Azure Stream Analytics pros

"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."

"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 product is very user-friendly."

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

"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."

"This product will assist us in saving costs in many ways: No longer need to continue paying high fees for proprietary software, reduce the number of software engineers needed to support the product, and achieve faster time to market by using this product for our middleware."

"Overall, Spring Cloud Data Flow is a really good solution and a lot cheaper than a lot of infrastructure provided by big companies like Google or Amazon."

"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."

More Spring Cloud Data Flow pros

Cons

"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."

"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."

"If something goes wrong, it's very hard to investigate what caused it and why."

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

"The initial setup is complex. It should be easier for new users who may not have too much Azure experience."

"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."

"There are some improvements that could be made, first of all in pricing, because right now the pricing is a bit unclear."

"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."

More Azure Stream Analytics cons

"The documentation on offer is not that good."

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

"The visual user interface could use some help; it needs improvement."

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

"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."

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

More Spring Cloud Data Flow cons

Pricing and Cost Advice

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

"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."

"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."

"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."

More Azure Stream Analytics pricing and cost advice

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

"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."

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

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Top Industries

By visitors reading reviews

Financial Services Firm

12%

Manufacturing Company

Computer Software Company

Comms Service Provider

Financial Services Firm

17%

Computer Software Company

10%

Retailer

Outsourcing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	1
Large Enterprise	5

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 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

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Compared 12% of the time

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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

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Apache Flink vs Spring Cloud Data Flow

Compared 9% of the time

TIBCO Spotfire vs Spring Cloud Data Flow

Compared 6% of the time

Cloudera DataFlow vs Spring Cloud Data Flow

Compared 6% of the time

TIBCO BusinessWorks vs Spring Cloud Data Flow

Compared 4% of the time

Apache Spark Streaming vs Spring Cloud Data Flow

Compared 4% of the time

More Spring Cloud Data Flow Competitors

Product Reports

Buyer's Guide

Azure Stream Analytics

August 2026

Download Azure Stream Analytics product report

Buyer's Guide

Spring Cloud Data Flow

July 2026

Download Spring Cloud Data Flow product report

Also Known As

ASA

No data available

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

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.

Broadcom

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

August 2026

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

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