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 7.6, and holds a 7.9% mindshare in the category. Microsoft is ranked #4 in SA , with an average rating of 8.1, and holds a 12.5% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 91% of Microsoft users who would recommend it.

Apache NiFi

Read 11 Apache NiFi reviews

2,418 Views
1,132 Comparison Views

90% willing to recommend

Azure Stream Analytics

Read 24 Azure Stream Analytics reviews

6,698 Views
5,740 Comparison Views

91% willing to recommend

Apache NiFi

Azure Stream Analytics

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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, Spot by NetApp and others in Compute Service.

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Buyer's Guide

Compute Service

December 2024

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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 (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 7.9%, up 6.0% compared to last year.
Azure Stream Analytics, on the other hand, focuses on Streaming Analytics, holds 12.5% mindshare, down 13.6% since last year.

Compute Service

Streaming Analytics

Featured Reviews

Bruno_Silva

Partner at Timestamp, SA

Very easy to schedule jobs that realize improvements and monetize

The use case templates could be more precise to typical business needs. Available templates and model workflows are very high-level so don't really match real needs. It would help to have templates that allow us to see business opportunities. It would help to be able to copy workflow to another device rather than having to ingest it.

Read full review

Sudhendra Umarji

Technical Architect at Infosys

Easy to set up and user-friendly, but could be priced better

I haven't come across missing items. It does what I need it to do. The pricing is a little bit high. The UI should be a little bit better from a usability perspective. The endpoint, if you are outsourcing to a third party, should have easier APIs. I'd like to have more destination sources available to us.

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."

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

"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."

"Visually, this is a good product."

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

"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."

"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."

More Apache NiFi pros

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

"It's easy to implement and maintain pipelines with minimal complexity."

"I like the way the UI looks, and the real-time analytics service is aligned to this. That can be helpful if I have to use this on a production service."

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

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

"It's a product that can scale."

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

"The solution's technical support is good."

More Azure Stream Analytics pros

Cons

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

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

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

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

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

"More features must be added to the product."

More Apache NiFi cons

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

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

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

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

"Easier scalability and more detailed job monitoring features would be helpful."

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

"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."

"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

"I used the tool's free version."

"The solution is open-source."

"It's an open-source solution."

"We use the free version of Apache NiFi."

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

"The current price is substantial."

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

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

"Azure Stream Analytics is a little bit expensive."

"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."

"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

19%

Computer Software Company

15%

Manufacturing Company

Retailer

Computer Software Company

15%

Financial Services Firm

14%

Manufacturing Company

Insurance Company

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?

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

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?

Stream Analytics is cheaper, especially for small-scale requirements or telemetry needs. However, for enterprise-level data handling, structured streaming with Databricks might be more cost-effecti...

See all answers

What needs improvement with Azure Stream Analytics?

More flexibility in terms of writing queries and accommodating additional facilities would be beneficial. The complexity of handling messages that need decoding and contain different characters sho...

See all answers

Comparisons

Google Cloud Dataflow vs Apache NiFi

Compared 30% of the time

AWS Lambda vs Apache NiFi

Compared 25% of the time

Apache Spark vs Apache NiFi

Compared 13% of the time

AWS Fargate vs Apache NiFi

Compared 8% of the time

Apache Storm vs Apache NiFi

Compared 6% of the time

More Apache NiFi Competitors

Databricks vs Azure Stream Analytics

Compared 27% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 22% of the time

Amazon MSK vs Azure Stream Analytics

Compared 20% of the time

Apache Flink vs Azure Stream Analytics

Compared 13% of the time

AWS Lambda vs Azure Stream Analytics

Compared 1% of the time

More Azure Stream Analytics Competitors

Product Reports

Buyer's Guide

Apache NiFi

December 2024

Download Apache NiFi product report

Buyer's Guide

Azure Stream Analytics

December 2024

Download Azure Stream Analytics product report

Also Known As

No data available

ASA

Learn More

Apache

Microsoft

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.

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

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: December 2024.

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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.