We performed a comparison between Amazon Kinesis and Apache Spark Streaming 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 scalability is pretty good."
"Everything is hosted and simple."
"Great auto-scaling, auto-sharing, and auto-correction features."
"Amazon Kinesis also provides us with plenty of flexibility."
"Amazon Kinesis has improved our 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."
"The solution works well in rather sizable environments."
"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."
"The solution is very stable and reliable."
"As an open-source solution, using it is basically free."
"The solution is better than average and some of the valuable features include efficiency and stability."
"Kinesis is good for Amazon Cloud but not as suitable for other cloud vendors."
"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."
"I think the default settings are far too low."
"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."
"Could include features that make it easier to scale."
"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."
"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."
"Lacks first in, first out queuing."
"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."
"The solution itself could be easier to use."
"We would like to have the ability to do arbitrary stateful functions in Python."
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and respond instantly instead of having to wait until all your data is collected before the processing can begin.
Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.
Amazon Kinesis is ranked 2nd in Streaming Analytics with 10 reviews while Apache Spark Streaming is ranked 11th in Streaming Analytics with 3 reviews. Amazon Kinesis is rated 8.4, while Apache Spark Streaming is rated 7.6. The top reviewer of Amazon Kinesis writes "Easily replay your streaming data with this reliable solution". On the other hand, the top reviewer of Apache Spark Streaming writes "Mature and stable with good scalability". Amazon Kinesis is most compared with Azure Stream Analytics, Apache Flink, Confluent, Amazon MSK and Google Cloud Dataflow, whereas Apache Spark Streaming is most compared with Apache Flink, Azure Stream Analytics, Spring Cloud Data Flow, Databricks and Confluent. See our Amazon Kinesis vs. Apache Spark Streaming 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.