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Apache NiFi vs Azure Stream Analytics comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Apache NiFi
Average Rating
7.8
Number of Reviews
11
Ranking in other categories
Compute Service (8th)
Azure Stream Analytics
Average Rating
8.2
Number of Reviews
23
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 8.0%, up 5.9% compared to last year.
Azure Stream Analytics, on the other hand, focuses on Streaming Analytics, holds 12.6% mindshare, down 13.5% since last year.
Compute Service
Streaming Analytics
 

Featured Reviews

Arjun Pandey - PeerSpot reviewer
Oct 25, 2023
Good monitoring, metrics capabilities and provides ability to design processors with a single click
The good thing about Apache NiFi is that it has a concept called a flow file, and there's something called a flow file processor. The processor is the building block of your entire job. They have close to 500 processors for each purpose. For example, for reading from Kafka, Ni-Fi has a processor called "consumer Kafka". To write to S3, they have a processor called "put S3". Now, if I read from Kafka and write my own application, I'd need to ensure the library I'm using tracks my messages. I'd also need to handle any failures by rereading messages and ensuring acknowledgment. But all this complexity is already handled by Apache processor. They have around 500 processors, with a community investing significant effort into developing them. I can design your processor with a single click, export the entire workflow, and import it. The format is actionable, so NiFi is immediately set up. It's also distributed in nature so that I can scale it across nodes based on the workload. These nodes share their state. If one node goes down during processing, that data might be lost, but any subsequent data is safe. Such occurrences are rare. In essence, if you want a quick solution, Apache NiFi is a strong contender. There are other solutions like AirFlow and some paid pipeline options. AirFlow is open-source but can be complicated. For ETL or ERT solutions, there are pricier options. But if I need a pipeline that I can monitor step by step, Apache NiFi is a good choice. It integrates with Prometheus metrics, allowing me to embed them in my workflow. There's also a processor for integration with Slack, and I can receive notifications when the workflow is completed or fails. Another feature I appreciate is "back pressure," which NiFi handles automatically. It maintains its own queue and addresses back-pressure issues. If, for instance, an upstream entity isn't fast enough, items get stored in a queue, managed internally by NiFi's back pressure algorithm.
Sudhendra Umarji - PeerSpot reviewer
May 29, 2022
Easy to set up and user-friendly, but could be priced better
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. It is easy to set up. The solution is stable and reliable.  It's a…

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 initial setup is very easy."
"We can integrate the tool with other applications easily."
"The most valuable features of this solution are ease of use and implementation."
"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."
"The most valuable feature has been the range of clients and the range of connectors that we could use."
"Visually, this is a good product."
"The life cycle, report management and crash management features are great."
"It's a product that can scale."
"It's scalable as a cloud product."
"The most valuable features are the IoT hub and the Blob storage."
"Technical support is pretty helpful."
"It provides the capability to streamline multiple output components."
"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."
 

Cons

"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."
"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 tool should incorporate more tutorials for advanced use cases. It has tutorials for simple use cases."
"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 use case templates could be more precise to typical business needs."
"More features must be added to the product."
"The collection and analysis of historical data could be better."
"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."
"It is not complex, but it requires some development skills. When the data is sent from Azure Stream Analytics to Power BI, I don't have the access to modify the data. I can't customize or edit the data or do some queries. All queries need to be done in the Azure Stream Analytics."
"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."
"Its features for event imports and architecture could be enhanced."
"Sometimes when we connect Power BI, there is a delay or it throws up some errors, so we're not sure."
"The UI should be a little bit better from a usability perspective."
"Easier scalability and more detailed job monitoring features would be helpful."
 

Pricing and Cost Advice

"I used the tool's free version."
"It's an open-source solution."
"We use the free version of Apache NiFi."
"The solution is open-source."
"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."
"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."
"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 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."
"The current price is substantial."
"The cost of this solution is less than competitors such as Amazon or Google Cloud."
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Top Industries

By visitors reading reviews
Financial Services Firm
19%
Computer Software Company
16%
Manufacturing Company
8%
Retailer
6%
Computer Software Company
16%
Financial Services Firm
14%
Manufacturing Company
8%
Insurance Company
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What needs improvement with Apache NiFi?
The tool should incorporate more tutorials for advanced use cases. It has tutorials for simple use cases.
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...
What is your experience regarding pricing and costs for Azure Stream Analytics?
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'v...
What needs improvement with Azure Stream Analytics?
Azure Stream Analytics is challenging to customize because it's not very flexible. It's good for quickly setting up and implementing solutions, but for building complex data pipelines and engineeri...
 

Also Known As

No data available
ASA
 

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Overview

 

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: October 2024.
814,649 professionals have used our research since 2012.