We performed a comparison between Amazon Kinesis and Azure Stream Analytics based on our users’ reviews in five categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: Amazon Kinesis ultimately wins out in this comparison. According to reviews, Amazon Kinesis appears to be a more robust and high performing solution.
"One of the best features of Amazon Kinesis is the multi-partition."
"Great auto-scaling, auto-sharing, and auto-correction features."
"The scalability is pretty good."
"Amazon Kinesis has improved our ROI."
"I like the ease of use and how we can quickly get the configurations done, making it pretty straightforward and stable."
"What turns out to be most valuable is its integration with Lambda functions because you can process the data as it comes in. As soon as data comes, you'll fire a Lambda function to process a trench of data."
"The feature that I've found most valuable is the replay. That is one of the most valuable in our business. We are business-to-business so replay was an important feature - being able to replay for 24 hours. That's an important feature."
"Setting Amazon Kinesis up is quick and easy; it only takes a few minutes to configure the necessary settings and start using it."
"We find the query editor feature of this solution extremely valuable for our business."
"The life cycle, report management and crash management features are great."
"Provides deep integration with other Azure resources."
"The solution has a lot of functionality that can be pushed out to companies."
"The integrations for this solution are easy to use and there is flexibility in integrating the tool with Azure Stream Analytics."
"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 solution's most valuable feature is its ability to create a query using SQ."
"It provides the capability to streamline multiple output components."
"In order to do a successful setup, the person handling the implementation needs to know the solution very well. You can't just come into it blind and with little to no experience."
"In general, the pain point for us was that once the data gets into Kinesis there is no way for us to understand what's happening because Kinesis divides everything into shards. So if we wanted to understand what's happening with a particular shard, whether it is published or not, we could not. Even with the logs, if we want to have some kind of logging it is in the shard."
"I think the default settings are far too low."
"Lacks first in, first out queuing."
"The services which are described in the documentation could use some visual presentation because for someone who is new to the solution the documentation is not easy to follow or beginner friendly and can leave a person feeling helpless."
"It would be beneficial if Amazon Kinesis provided document based support on the internet to be able to read the data from the Kinesis site."
"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."
"One thing that would be nice would be a policy for increasing the number of Kinesis streams because that's the one thing that's constant. You can change it in real time, but somebody has to change it, or you have to set some kind of meter. So, auto-scaling of adding and removing streams would be nice."
"The solution’s customer support could be improved."
"Sometimes when we connect Power BI, there is a delay or it throws up some errors, so we're not sure."
"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."
"The UI should be a little bit better from a usability perspective."
"The collection and analysis of historical data could be better."
"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."
"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."
Amazon Kinesis is ranked 1st in Streaming Analytics with 24 reviews while Azure Stream Analytics is ranked 3rd in Streaming Analytics with 22 reviews. Amazon Kinesis is rated 8.0, while Azure Stream Analytics is rated 8.2. The top reviewer of Amazon Kinesis writes "Used for media streaming and live-streaming data". On the other hand, the top reviewer of Azure Stream Analytics writes "Easy to set up and user-friendly, but could be priced better". Amazon Kinesis is most compared with Amazon MSK, Confluent, Apache Flink, Google Cloud Dataflow and Apache Spark Streaming, whereas Azure Stream Analytics is most compared with Databricks, Amazon MSK, Apache Flink, Apache Spark and Apache Spark Streaming. See our Amazon Kinesis vs. Azure Stream Analytics report.
See our list of best Streaming Analytics vendors.
We monitor all Streaming Analytics 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.