Apache NiFi vs Azure Stream Analytics comparison

The compared Apache and Microsoft solutions aren't in the same category. Apache is ranked #8 in CS , with an average rating of 8.3, and holds a 9.3% mindshare in the category. Microsoft is ranked #4 in SA , with an average rating of 7.1, and holds a 7.2% mindshare. Additionally, 92% of Apache users are willing to recommend the solution, compared to 90% of Microsoft users who would recommend it.

Apache NiFi

Read 13 Apache NiFi reviews

1,719 Views
1,100 Comparison Views

92% willing to recommend

Azure Stream Analytics

Read 30 Azure Stream Analytics reviews

3,093 Views
2,815 Comparison Views

90% willing to recommend

Apache NiFi

Azure Stream Analytics

Comparison Buyer's Guide

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Executive Summary

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

Find out what your peers are saying about Amazon Web Services (AWS), Apache, Zadara and others in Compute Service.

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Buyer's Guide

Compute Service

October 2025

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Helped 872,869 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

Apache NiFi

Average Rating

7.8

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

Compute Service (8th)

Azure Stream Analytics

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Streaming Analytics (4th)

Mindshare comparison

Apache NiFi and Azure Stream Analytics aren’t in the same category and serve different purposes. Apache NiFi is designed for Compute Service and holds a mindshare of 9.3%, up 8.0% compared to last year.
Azure Stream Analytics, on the other hand, focuses on Streaming Analytics, holds 7.2% mindshare, down 11.8% since last year.

Compute Service Market Share Distribution
Product	Market Share (%)
Apache NiFi	9.3%
AWS Lambda	16.6%
AWS Batch	15.6%
Other	58.5%

Compute Service

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Azure Stream Analytics	7.2%
Apache Flink	14.4%
Databricks	11.8%
Other	66.6%

Streaming Analytics

Featured Reviews

Bharghava Raghavendra Beesa

Senior Developer at Infosys

The tool enables effective data transformation and integration

There are some areas for improvement, particularly with record-level tasks that take a bit of time. The quality of JSON data processing could be improved, as JSON workloads require manual conversions without a specific process. Enhancing features related to alerting would be helpful, including mobile alerts for pipeline issues. Integration with mobile devices for error alerts would simplify information delivery.

Read full review

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.

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

"Apache NiFi is user-friendly. Its most valuable features for handling large volumes of data include its multitude of integrated endpoints and clients and the ability to create cron jobs to run tasks at regular intervals."

"The user interface is good and makes it easy to design very popular workflows."

"Visually, this is a good product."

"It is highly effective for handling real-time data by working with APIs for immediate and continuous data extraction."

"The initial setup is very easy. I would rate my experience with the initial setup a ten out of ten, where one point is difficult, and ten points are easy."

"The most valuable features of this solution are ease of use and implementation."

"The most valuable feature has been the range of clients and the range of connectors that we could use."

"It's an automated flow, where you can build a flow from source to destination, then do the transformation in between."

More Apache NiFi pros

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

"Technical support is pretty helpful."

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

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

"It's a product that can scale."

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

"We use Azure Stream Analytics for simulation and internal activities."

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

More Azure Stream Analytics pros

Cons

"The quality of JSON data processing could be improved, as JSON workloads require manual conversions without a specific process."

"There should be a better way to integrate a development environment with local tools."

"The overall stability of this solution could be improved. In a future release, we would like to have access to more features that could be used in a parallel way. This would provide more freedom with processing."

"The tool should incorporate more tutorials for advanced use cases. It has tutorials for simple use cases."

"There is room for improvement in integration with SSO. For example, NiFi does not have any integration with SSO. And if I want to give some kind of rollback access control across the organization. That is not possible."

"We run many jobs, and there are already large tables. When we do not control NiFi on time, all reports fail for the day. So it's pretty slow to control, and it has to be improved."

"There are some claims that NiFi is cloud-native but we have tested it, and it's not."

"The use case templates could be more precise to typical business needs."

More Apache NiFi cons

"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."

"Regarding technical support for Azure Stream Analytics, it's not good."

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

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

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

"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."

"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."

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

More Azure Stream Analytics cons

Pricing and Cost Advice

"We use the free version of Apache NiFi."

"The solution is open-source."

"I used the tool's free version."

"It's an open-source solution."

"The current price is substantial."

"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 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."

"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."

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

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

More Azure Stream Analytics pricing and cost advice

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Top Industries

By visitors reading reviews

Manufacturing Company

13%

Computer Software Company

13%

Financial Services Firm

12%

Retailer

Financial Services Firm

15%

Computer Software Company

13%

Manufacturing Company

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	3
Large Enterprise	10

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	3
Large Enterprise	18

Questions from the Community

What do you like most about Apache NiFi?

The initial setup is very easy.

See all answers

What is your experience regarding pricing and costs for Apache NiFi?

Apache NiFi is open-source and free. Its integration with systems like Cloudera can be expensive, but Apache NiFi itself presents the best pricing as a standalone tool.

See all answers

What needs improvement with Apache NiFi?

The logging system of Apache NiFi needs improvement. It is difficult to debug compared to Airflow ( /products/apache-airflow-reviews ), where task details and issues are clear. With Apache NiFi, I ...

See all answers

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

Comparisons

AWS Lambda vs Apache NiFi

Compared 26% of the time

Google Cloud Dataflow vs Apache NiFi

Compared 21% of the time

Apache Spark vs Apache NiFi

Compared 16% of the time

Amazon EC2 vs Apache NiFi

Compared 11% of the time

Oracle Compute Cloud Service vs Apache NiFi

Compared 5% of the time

More Apache NiFi Competitors

Amazon MSK vs Azure Stream Analytics

Compared 22% of the time

Databricks vs Azure Stream Analytics

Compared 18% of the time

Apache Flink vs Azure Stream Analytics

Compared 18% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 16% of the time

PubSub+ Platform vs Azure Stream Analytics

Compared 2% of the time

More Azure Stream Analytics Competitors

Product Reports

Buyer's Guide

Apache NiFi

November 2025

Download Apache NiFi product report

Buyer's Guide

Azure Stream Analytics

November 2025

Download Azure Stream Analytics product report

Also Known As

No data available

ASA

Overview

Apache NiFi is an easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Apache

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

Sample Customers

Macquarie Telecom Group, Dovestech, Slovak Telekom, Looker, Hastings Group

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

Find out what your peers are saying about Amazon Web Services (AWS), Apache, Zadara and others in Compute Service. Updated: October 2025.

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