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 user interface is good and makes it easy to design very popular workflows."
"The initial setup is very easy."
"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."
"We can integrate the tool with other applications easily."
"Visually, this is a good product."
"The most valuable features of this solution are ease of use and implementation."
"It's an automated flow, where you can build a flow from source to destination, then do the transformation in between."
"The solution's most valuable feature is its ability to create a query using SQ."
"The solution's technical support is good."
"The life cycle, report management and crash management features are great."
"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."
"It's a product that can scale."
"Technical support is pretty helpful."
"The most valuable features are the IoT hub and the Blob storage."
"It provides the capability to streamline multiple output components."
"More features must be added to the product."
"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 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."
"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."
"There should be a better way to integrate a development environment with local tools."
"The initial setup is complex."
"The solution could be improved by providing better graphics and including support for UI and UX testing."
"The solution’s customer support could be improved."
"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 collection and analysis of historical data could be better."
"Sometimes when we connect Power BI, there is a delay or it throws up some errors, so we're not sure."
"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."
"The solution offers a free trial, however, it is too short."
Apache NiFi is ranked 8th in Compute Service with 10 reviews while Azure Stream Analytics is ranked 4th 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.
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.