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 7.9% mindshare in the category. Microsoft is ranked #3 in SA , with an average rating of 8.0, and holds a 11.4% mindshare. Additionally, 91% of Apache users are willing to recommend the solution, compared to 92% of Microsoft users who would recommend it.

Apache NiFi

Read 12 Apache NiFi reviews

2,136 Views
991 Comparison Views

91% willing to recommend

Azure Stream Analytics

Read 25 Azure Stream Analytics reviews

5,816 Views
4,950 Comparison Views

92% 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.

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Buyer's Guide

Compute Service

January 2025

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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

Apache NiFi

Average Rating

7.8

Number of Reviews

Ranking in other categories

Compute Service (8th)

Azure Stream Analytics

Average Rating

8.0

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Streaming Analytics (3rd)

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 7.9%, up 6.5% compared to last year.
Azure Stream Analytics, on the other hand, focuses on Streaming Analytics, holds 11.4% mindshare, down 12.9% 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 most valuable feature has been the range of clients and the range of connectors that we could use."

"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."

"Visually, this is a good product."

"The initial setup is very easy."

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

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

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

More Apache NiFi pros

"The solution has a lot of functionality that can be pushed out to companies."

"It's a product that can scale."

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

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

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

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

"The solution's technical support is good."

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

More Azure Stream Analytics pros

Cons

"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."

"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."

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

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

"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."

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

More Apache NiFi cons

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

"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."

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

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

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

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

"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."

"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

Pricing and Cost Advice

"We use the free version of Apache NiFi."

"It's an open-source solution."

"I used the tool's free version."

"The solution is open-source."

"The current price is substantial."

"Azure Stream Analytics is a little bit expensive."

"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."

"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."

"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."

"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."

More Azure Stream Analytics pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

14%

Manufacturing Company

Retailer

Computer Software Company

15%

Financial Services Firm

15%

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?

I used the tool's free version.

See all answers

What needs improvement with Apache NiFi?

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?

When I purchased the product, it was comparable to other Azure products. From my point of view, it should be cheaper now, considering the years since its release. The Azure solution is better now, ...

See all answers

What needs improvement with Azure Stream Analytics?

See all answers

Comparisons

Google Cloud Dataflow vs Apache NiFi

Compared 31% of the time

AWS Lambda vs Apache NiFi

Compared 29% of the time

Apache Spark vs Apache NiFi

Compared 13% of the time

AWS Fargate vs Apache NiFi

Compared 9% of the time

Apache Storm vs Apache NiFi

Compared 4% of the time

More Apache NiFi Competitors

Databricks vs Azure Stream Analytics

Compared 28% of the time

Amazon MSK vs Azure Stream Analytics

Compared 21% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 20% of the time

Apache Flink vs Azure Stream Analytics

Compared 14% 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

February 2025

Download Apache NiFi product report

Buyer's Guide

Azure Stream Analytics

February 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, Spot by NetApp and others in Compute Service. Updated: January 2025.

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