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 solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."
"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."
"I have worked in companies that build tools in-house. They face scaling challenges."
"The most valuable feature is that it has a pretty robust way of capturing things."
"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."
"I find almost all features valuable, especially the timing and fast pace movement."
"Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive."
"The solution is very stable and reliable."
"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."
"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."
"As an open-source solution, using it is basically free."
"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."
"The solution is better than average and some of the valuable features include efficiency and stability."
"It's the fastest solution on the market with low latency data on data transformations."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
"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."
"Amazon Kinesis involved a more complex setup and configuration than Azure Event Hub."
"We were charged high costs for the solution’s enhanced fan-out feature."
"Could include features that make it easier to scale."
"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 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 is good for Amazon Cloud but not as suitable for other cloud vendors."
"One area for improvement in the solution is the file size limitation of 10 Mb. My company works with files with a larger file size. The batch size and throughput also need improvement in Amazon Kinesis."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"It was resource-intensive, even for small-scale applications."
"We would like to have the ability to do arbitrary stateful functions in Python."
"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."
"In terms of improvement, the UI could be better."
"The initial setup is quite complex."
"Integrating event-level streaming capabilities could be beneficial."
"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 1st in Streaming Analytics with 24 reviews while Apache Spark Streaming is ranked 8th in Streaming Analytics with 9 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, Amazon MSK, Confluent, Apache Flink and Databricks, whereas Apache Spark Streaming is most compared with Spring Cloud Data Flow, Azure Stream Analytics, Apache Pulsar, Confluent and Starburst Enterprise. 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.