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

Download the report

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.

To learn more, read our detailed Compute Service Report (Updated: August 2025).

Buyer's Guide

Compute Service

August 2025

Download the complete report

Helped 866,088 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 Market Share Distribution
Product	Market Share (%)
Apache NiFi	9.3%
AWS Lambda	19.4%
AWS Batch	18.9%
Other	52.400000000000006%

Compute Service

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Azure Stream Analytics	8.8%
Apache Flink	14.5%
Databricks	13.5%
Other	63.2%

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

"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 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."

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

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

"The initial setup is very easy."

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

More Apache NiFi pros

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

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

"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."

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

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

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

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

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

More Azure Stream Analytics pros

Cons

"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."

"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."

"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."

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

"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."

More Apache NiFi cons

"There is a lack of technical support from Microsoft's local office, particularly in Taiwan."

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

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

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

"The UI should be a little bit better from a usability perspective."

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

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

More Azure Stream Analytics cons

Pricing and Cost Advice

"The solution is open-source."

"We use the free version of Apache NiFi."

"I used the tool's free version."

"It's an open-source solution."

"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."

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

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

"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 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."

"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

See which vendors are best for you

Use our free recommendation engine to learn which Compute Service solutions are best for your needs.

See recommendations

866,088 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

14%

Computer Software Company

12%

Manufacturing Company

11%

Retailer

Financial Services Firm

14%

Computer Software Company

14%

Manufacturing Company

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	5
Large Enterprise	8

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	3
Large Enterprise	16

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 26% 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 17% 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.

DOWNLOAD NOW

866,088 professionals have used our research since 2012.

We monitor all Compute Service reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.