Azure Stream Analytics vs Cloudera DataFlow comparison

Why PeerSpot?

Microsoft and Cloudera are both solutions in the Streaming Analytics category. Microsoft is ranked #2 with an average rating of 6.7, while Cloudera is ranked #19 with an average rating of 8.0. Microsoft holds a 6.1% mindshare in SA, compared to Cloudera’s 3.2% mindshare. Additionally, 90% of Microsoft users are willing to recommend the solution, compared to 80% of Cloudera users who would recommend it.

Azure Stream Analytics

Read 30 Azure Stream Analytics reviews

6,679 Views
6,145 Comparison Views

90% willing to recommend

Cloudera DataFlow

Read 5 Cloudera DataFlow reviews

2,512 Views
2,361 Comparison Views

80% willing to recommend

Azure Stream Analytics

Cloudera DataFlow

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 17, 2024

Azure Stream Analytics and Cloudera DataFlow compete in the realm of data processing and analytics. Azure Stream Analytics seems to have an edge in environments leveraging the Azure ecosystem due to its effective integration and cost-effectiveness, while Cloudera DataFlow excels in robust data handling and scalability, favoring high-performance needs.

Features: Azure Stream Analytics stands out for its real-time data streaming, seamless integration with Azure services, and user-friendly interface, making it ideal for rapid deployment. Cloudera DataFlow offers comprehensive data flow management, scalable stream processing, and flexibility across hybrid environments, appealing to users managing complex data flows.

Room for Improvement: Azure Stream Analytics could enhance its abilities by expanding integrations outside of Azure and improving scalability options for diverse environments. Interface customization and advanced analytical capabilities can also be areas of focus. Cloudera DataFlow could improve by simplifying its deployment process, enhancing user interface intuitiveness, and reducing the initial setup cost for better accessibility.

Ease of Deployment and Customer Service: Azure Stream Analytics provides a straightforward deployment process backed by efficient customer support, suiting users new to analytics platforms. Cloudera DataFlow, though catering to enterprise needs, involves a more complex deployment process but offers strong customer service resources for tailored solutions.

Pricing and ROI: Azure Stream Analytics presents a budget-friendly pricing model with a strong ROI, benefiting organizations utilizing the Azure cloud. Cloudera DataFlow, although potentially incurring a higher initial cost, offers significant ROI through its powerful data handling capabilities, appealing to firms requiring robust solutions that justify the investment.

To learn more, read our detailed Azure Stream Analytics vs. Cloudera DataFlow Report (Updated: September 2026).

Buyer's Guide

Azure Stream Analytics vs. Cloudera DataFlow

September 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

Cloudera DataFlow

Ranking in Streaming Analytics

19th

Average Rating

7.4

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of October 2026, in the Streaming Analytics category, the mindshare of Azure Stream Analytics is 6.1%, down from 7.5% compared to the previous year. The mindshare of Cloudera DataFlow is 3.2%, up from 1.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Azure Stream Analytics	6.1%
Cloudera DataFlow	3.2%
Other	90.7%

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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Mohamed-Saied

Senior Data Architect at Teradata Corporation

Efficient data integration and workflow scheduling elevate project performance

Cloudera DataFlow is used as an ETL or ELT solution within Cloudera's data pipeline. Our organization heavily relies on it for data ingestion, transformation, and warehousing. It is also used daily for operational tasks, and it integrates well within Cloudera's ecosystem for high performance and…

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

"From the end user's perspective, Stream Analytics forms the main backbone for the entire pipeline and all the technologies."

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

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

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

"The biggest improvement for us has been that it now takes much less time for us to receive valuable information."

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

"The stability is okay and we are satisfied with it."

"Since implementing Azure, we have been able to monitor things very easily."

More Azure Stream Analytics pros

"The initial setup was not so difficult"

"Cloudera DataFlow is fully compatible with Cloudera's ecosystem and offers high efficiency through native connectors for various ecosystems."

"The most effective features are data management and analytics."

"DataFlow's performance is okay."

"This solution is very scalable and robust."

Cons

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

"The pricing is a little bit high."

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

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

"Having worked with some good monitoring tools, this is not that helpful as you cannot build analytics and predictions on these types of monitoring."

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

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

More Azure Stream Analytics cons

"It is not easy to use the R language. Though I don't know if it's possible, I believe it is possible, but it is not the best language for machine learning."

"It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists."

"Cloudera DataFlow's UI interface could be enhanced significantly. Memory handling can also be improved to be better than it is today."

"Although their workflow is pretty neat, it still requires a lot of transformation coding; especially when it comes to Python and other demanding programming languages."

Pricing and Cost Advice

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

"Azure Stream Analytics is a little bit expensive."

"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 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."

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

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

"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."

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

More Azure Stream Analytics pricing and cost advice

"DataFlow isn't expensive, but its value for money isn't great."

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Top Industries

By visitors reading reviews

Financial Services Firm

10%

Manufacturing Company

10%

Comms Service Provider

10%

University

Financial Services Firm

13%

Construction Company

13%

Comms Service Provider

12%

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

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 needs improvement with Cloudera DataFlow?

Cloudera DataFlow's UI interface could be enhanced significantly. Memory handling can also be improved to be better than it is today.

See all answers

What is your primary use case for Cloudera DataFlow?

See all answers

What advice do you have for others considering Cloudera DataFlow?

Cloudera DataFlow is fully compatible with Cloudera's ecosystem and offers high efficiency through native connectors for various ecosystems. However, the learning curve is high, and there is a shor...

See all answers

Comparisons

Databricks vs Azure Stream Analytics

Compared 13% of the time

Confluent vs Azure Stream Analytics

Compared 9% of the time

Apache Spark Streaming vs Azure Stream Analytics

Compared 8% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 8% of the time

Apache Flink vs Azure Stream Analytics

Compared 7% of the time

More Azure Stream Analytics Competitors

Spring Cloud Data Flow vs Cloudera DataFlow

Compared 13% of the time

Databricks vs Cloudera DataFlow

Compared 13% of the time

WSO2 Stream Processor vs Cloudera DataFlow

Compared 12% of the time

Confluent vs Cloudera DataFlow

Compared 10% of the time

Qlik Talend Cloud vs Cloudera DataFlow

Compared 9% of the time

More Cloudera DataFlow Competitors

Product Reports

Buyer's Guide

Azure Stream Analytics

October 2026

Download Azure Stream Analytics product report

Buyer's Guide

Streaming Analytics

September 2026

Download Cloudera DataFlow product report

Also Known As

ASA

CDF, Hortonworks DataFlow, HDF

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

Cloudera DataFlow is a scalable data integration platform offering high performance through native connections with Cloudera ecosystems like Hive, Impala, and Spark, facilitating robust data management and analytics.

Cloudera DataFlow excels in delivering comprehensive data analysis with end-to-end workflow scheduling and stands out for its high throughput and effective integration capabilities. However, users note areas needing improvement, such as transformation coding complexity, limited language support, and memory handling. While it plays an essential ETL or ELT role in Cloudera's data pipeline, providing seamless data ingestion, transformation, and warehousing, the platform's restriction to its environment and the setup's complexity remain points of user concern.

What are the key features of Cloudera DataFlow?

Scalability: Offers robust performance across various data workloads.
Native Connectivity: Seamless integration with Cloudera ecosystems like Hive and Spark for high efficiency.
Workflow Scheduling: Supports comprehensive end-to-end scheduling capabilities.
Data Management: High throughput and effective data integration capabilities.

What benefits should users expect?

High Performance: Strong throughput and efficient workload processing.
Seamless Integration: Smooth operations within Cloudera's ecosystem.
Comprehensive Analysis: Supports advanced analytics without extensive coding.

Industries use Cloudera DataFlow for applications like sentiment analysis, fraud detection, and product royalty analysis. It is widely deployed for stream analytics and module development in telecommunications, functioning as a critical tool for data ingestion and transformation, ensuring efficient operational tasks.

Cloudera

Sample Customers

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

Clearsense

Buyer's Guide

Azure Stream Analytics vs. Cloudera DataFlow

September 2026

Free Report: Azure Stream Analytics vs. Cloudera DataFlow

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