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."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 find almost all features valuable, especially the timing and fast pace movement."
"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."
"Everything is hosted and simple."
"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."
"Great auto-scaling, auto-sharing, and auto-correction features."
"One of the best features of Amazon Kinesis is the multi-partition."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"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 product's installation process is easy...The tool's maintenance part is somewhat easy."
"The support team is good and it's easy to use."
"It is a scalable solution."
"The best feature of Google Cloud Dataflow is its practical connectedness."
"The solution allows us to program in any language we desire."
"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."
"Kinesis Data Analytics needs to be improved somewhat. It's SQL based data but it is not as user friendly as MySQL or Athena tools."
"The solution has a two-minute maximum time delay for live streaming, which could be reduced."
"Kinesis is good for Amazon Cloud but not as suitable for other cloud vendors."
"Lacks first in, first out queuing."
"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."
"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."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
"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."
"The solution's setup process could be more accessible."
"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."
"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."
"They should do a market survey and then make improvements."
"The technical support has slight room for improvement."
"The deployment time could also be reduced."
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.
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.