We performed a comparison between Amazon Kinesis and Google Cloud Dataflow based on real PeerSpot user reviews.
Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The solution works well in rather sizable environments."
"Kinesis is a fully managed program streaming application. You can manage any infrastructure. It is also scalable. Kinesis can handle any amount of data streaming and process data from hundreds, thousands of processes in every source with very low latency."
"Setting Amazon Kinesis up is quick and easy; it only takes a few minutes to configure the necessary settings and start using it."
"I like the ease of use and how we can quickly get the configurations done, making it pretty straightforward and stable."
"Great auto-scaling, auto-sharing, and auto-correction features."
"Everything is hosted and simple."
"The solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."
"The integration capabilities of the product are good."
"It is a scalable solution."
"The support team is good and it's easy to use."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"I don't need a server running all the time while using the tool. It is also easy to setup. The product offers a pay-as-you-go service."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"The product's installation process is easy...The tool's maintenance part is somewhat easy."
"The most valuable features of Google Cloud Dataflow are the integration, it's very simple if you have the complete stack, which we are using. It is overall very easy to use, user-friendly friendly, and cost-effective if you know how to use it. The solution is very flexible for programmers, if you know how to do scripts or program in Python or any other language, it's extremely easy to use."
"The service is relatively cheap compared to other batch-processing engines."
"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."
"Kinesis can be expensive, especially when dealing with large volumes of data."
"Kinesis is good for Amazon Cloud but not as suitable for other cloud vendors."
"For me, especially with video streams, there's sometimes a kind of delay when the data has to be pumped to other services. This delay could be improved in Kinesis, or especially the Kinesis Video Streams, which is being used for different use cases for Amazon Connect. With that improvement, a lot of other use cases of Amazon Connect integrating with third-party analytic tools would be easier."
"We were charged high costs for the solution’s enhanced fan-out feature."
"The price is not much cheaper. So, there is room for improvement in the pricing."
"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."
"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."
"Google Cloud Dataflow should include a little cost optimization."
"The deployment time could also be reduced."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
"The technical support has slight room for improvement."
"When I deploy the product in local errors, a lot of errors pop up which are not always caught. The solution's error logging is bad. It can take a lot of time to debug the errors. It needs to have better logs."
"The authentication part of the product is an area of concern where improvements are required."
"I would like Google Cloud Dataflow to be integrated with IT data flow and other related services to make it easier to use as it is a complex tool."
"Google Cloud Data Flow can improve by having full simple integration with Kafka topics. It's not that complicated, but it could improve a bit. The UI is easy to use but the experience could be better. There are other tools available that do a better job."
Amazon Kinesis is ranked 1st in Streaming Analytics with 24 reviews while Google Cloud Dataflow is ranked 7th in Streaming Analytics with 10 reviews. Amazon Kinesis is rated 8.0, while Google Cloud Dataflow is rated 7.8. The top reviewer of Amazon Kinesis writes "Used for media streaming and live-streaming data". On the other hand, the top reviewer of Google Cloud Dataflow writes "Easy to use for programmers, user-friendly, and scalable". Amazon Kinesis is most compared with Azure Stream Analytics, Amazon MSK, Confluent, Apache Flink and Apache Spark Streaming, whereas Google Cloud Dataflow is most compared with Databricks, Apache NiFi, Amazon MSK, Spring Cloud Data Flow and Apache Flink. See our Amazon Kinesis vs. Google Cloud Dataflow report.
See our list of best Streaming Analytics vendors.
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