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.
"Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive."
"Great auto-scaling, auto-sharing, and auto-correction features."
"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."
"Amazon Kinesis has improved our ROI."
"The integration capabilities of the product are good."
"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."
"One of the best features of Amazon Kinesis is the multi-partition."
"Its scalability is very high. There is no maintenance and there is no throughput latency. I think data scalability is high, too. You can ingest gigabytes of data within seconds or milliseconds."
"The solution has a lot of functionality that can be pushed out to companies."
"The solution's most valuable feature is its ability to create a query using SQ."
"The solution's technical support is good."
"It's scalable as a cloud product."
"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 integrations for this solution are easy to use and there is flexibility in integrating the tool with Azure Stream Analytics."
"I like all the connected ecosystems of Microsoft, it is really good with other BI tools that are easy to connect."
"The way it organizes data into tables and dashboards is very helpful."
"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."
"If there were better documentation on optimal sharding strategies then it would be helpful."
"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."
"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."
"Amazon Kinesis involved a more complex setup and configuration than Azure Event Hub."
"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."
"Something else to mention is that we use Kinesis with Lambda a lot and the fact that you can only connect one Stream to one Lambda, I find is a limiting factor. I would definitely recommend to remove that constraint."
"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."
"The solution doesn't handle large data packets very efficiently, which could be improved upon."
"I would like to have a contact individual at Microsoft."
"If something goes wrong, it's very hard to investigate what caused it and why."
"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."
"Sometimes when we connect Power BI, there is a delay or it throws up some errors, so we're not sure."
"The solution could be improved by providing better graphics and including support for UI and UX testing."
"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 UI should be a little bit better from a usability perspective."
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.
