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, Zadara and others in Compute Service."The initial setup is very 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."
"We can integrate the tool with other applications easily."
"It's an automated flow, where you can build a flow from source to destination, then do the transformation in between."
"The user interface is good and makes it easy to design very popular workflows."
"Visually, this is a good product."
"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."
"Provides deep integration with other Azure resources."
"The solution has a lot of functionality that can be pushed out to companies."
"The most valuable features are the IoT hub and the Blob storage."
"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."
"The solution's most valuable feature is its ability to create a query using SQ."
"We find the query editor feature of this solution extremely valuable for our business."
"The solution's technical support is good."
"I like all the connected ecosystems of Microsoft, it is really good with other BI tools that are easy to connect."
"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."
"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."
"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."
"The use case templates could be more precise to typical business needs."
"There are some claims that NiFi is cloud-native but we have tested it, and it's not."
"More features must be added to the product."
"The solution doesn't handle large data packets very efficiently, which could be improved upon."
"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."
"Its features for event imports and architecture could be enhanced."
"The collection and analysis of historical data could be better."
"I would like to have a contact individual at Microsoft."
"The UI should be a little bit better from a usability perspective."
"The initial setup is complex."
"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."
Apache NiFi is ranked 8th in Compute Service with 11 reviews while Azure Stream Analytics is ranked 3rd in Streaming Analytics with 22 reviews. Apache NiFi is rated 7.8, while Azure Stream Analytics is rated 8.2. The top reviewer of Apache NiFi writes "Allows the creation and use of custom functions to achieve desired functionality but limitation in handling monthly transactions due to a lack of partitioning for dates". On the other hand, the top reviewer of Azure Stream Analytics writes "Easy to set up and user-friendly, but could be priced better". Apache NiFi is most compared with Google Cloud Dataflow, AWS Lambda, Apache Spark, Apache Storm and AWS Fargate, whereas Azure Stream Analytics is most compared with Amazon Kinesis, Databricks, Amazon MSK, Apache Flink and AWS Lambda.
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