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."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's technical support is flawless."
"Great auto-scaling, auto-sharing, and auto-correction features."
"The scalability is pretty good."
"The solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."
"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."
"The management and analytics are valuable features."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
"The solution is better than average and some of the valuable features include efficiency and stability."
"Apache Spark Streaming's most valuable feature is near real-time analytics. The developers can build APIs easily for a code-steaming pipeline. The solutions have an ecosystem of integration with other stock services."
"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."
"As an open-source solution, using it is basically free."
"Apache Spark Streaming was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple."
"The solution is very stable and reliable."
"It's the fastest solution on the market with low latency data on data transformations."
"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."
"Kinesis is good for Amazon Cloud but not as suitable for other cloud vendors."
"Snapshot from the the from the the stream of the data analytic I have already on the cloud, do a snapshot to not to make great or to get the data out size of the web service. But to stop the process and restart a few weeks later when I have more data or more available of the client teams."
"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."
"Kinesis can be expensive, especially when dealing with large volumes of data."
"Amazon Kinesis should improve its limits."
"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."
"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."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"It was resource-intensive, even for small-scale applications."
"The initial setup is quite complex."
"In terms of improvement, the UI could be better."
"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."
"The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better."
Amazon Kinesis is ranked 2nd in Streaming Analytics with 21 reviews while Apache Spark Streaming is ranked 8th in Streaming Analytics with 8 reviews. Amazon Kinesis is rated 8.0, while Apache Spark Streaming is rated 8.0. The top reviewer of Amazon Kinesis writes "Used for media streaming and live-streaming data". On the other hand, the top reviewer of Apache Spark Streaming writes "Easy integration, beneficial auto-scaling, and good open-sourced support community". Amazon Kinesis is most compared with Azure Stream Analytics, Apache Flink, Amazon MSK, Confluent and Databricks, whereas Apache Spark Streaming is most compared with Azure Stream Analytics, Spring Cloud Data Flow, Confluent, Apache Pulsar and Starburst Enterprise. See our Amazon Kinesis vs. Apache Spark Streaming report.
See our list of best Streaming Analytics vendors.
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