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 7.7, 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 8.8% mindshare. Additionally, 92% of Apache users are willing to recommend the solution, compared to 89% of Microsoft users who would recommend it.

Apache NiFi

Read 13 Apache NiFi reviews

2,215 Views
1,329 Comparison Views

92% willing to recommend

Azure Stream Analytics

Read 28 Azure Stream Analytics reviews

3,832 Views
3,487 Comparison Views

89% 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, Oracle and others in Compute Service.

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Buyer's Guide

Compute Service

August 2025

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Helped 865,295 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

6.7

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 8.8% mindshare, down 12.5% since last year.

Compute Service

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

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.

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 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 initial setup is very easy."

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

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

"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."

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

"We can integrate the tool with other applications easily."

More Apache NiFi pros

"The most valuable aspect is the SQL option that Azure Stream Analytics provides."

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

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

"I appreciate this solution because it leverages open-source technologies. It allows us to utilize the latest streaming solutions and it's easy to develop."

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

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

"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."

"I like the IoT part. We have mostly used Azure Stream Analytics services for it"

More Azure Stream Analytics pros

Cons

"More features must be added to the product."

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

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

"I think the UI interface needs to be more user-friendly."

"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 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."

"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."

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

More Apache NiFi cons

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

"The solution offers a free trial, however, it is too short."

"The solution’s customer support could be improved."

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

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

"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."

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

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

More Azure Stream Analytics cons

Pricing and Cost Advice

"The solution is open-source."

"I used the tool's free version."

"We use the free version of Apache NiFi."

"It's an open-source solution."

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

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

"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."

"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."

"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."

More Azure Stream Analytics pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

12%

Manufacturing Company

11%

Retailer

Financial Services Firm

15%

Computer Software Company

14%

Manufacturing Company

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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?

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

Comparisons

AWS Lambda vs Apache NiFi

Compared 25% of the time

Google Cloud Dataflow vs Apache NiFi

Compared 23% of the time

Apache Spark vs Apache NiFi

Compared 16% of the time

AWS Fargate vs Apache NiFi

Compared 8% of the time

Amazon EC2 Auto Scaling vs Apache NiFi

Compared 4% of the time

More Apache NiFi Competitors

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

Google Cloud Dataflow vs Azure Stream Analytics

Compared 2% of the time

More Azure Stream Analytics Competitors

Product Reports

Buyer's Guide

Apache NiFi

August 2025

Download Apache NiFi product report

Buyer's Guide

Azure Stream Analytics

August 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, Oracle and others in Compute Service. Updated: August 2025.

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