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."Everything is hosted and simple."
"Setting Amazon Kinesis up is quick and easy; it only takes a few minutes to configure the necessary settings and start using it."
"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."
"I like the ease of use and how we can quickly get the configurations done, making it pretty straightforward and stable."
"Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive."
"Great auto-scaling, auto-sharing, and auto-correction features."
"The solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
"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'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."
"It's the fastest solution on the market with low latency data on data transformations."
"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."
"The solution has a two-minute maximum time delay for live streaming, which could be reduced."
"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."
"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."
"I suggest integrating additional features, such as incorporating Amazon Pinpoint or Amazon Connect as bundled offerings, rather than deploying them as separate services."
"If there were better documentation on optimal sharding strategies then it would be helpful."
"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."
"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."
"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."
"The initial setup is quite complex."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"The solution itself could be easier to use."
"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."
Amazon Kinesis is ranked 1st in Streaming Analytics with 24 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, Amazon MSK, Confluent, Apache Flink and Databricks, whereas Apache Spark Streaming is most compared with Spring Cloud Data Flow, Azure Stream Analytics, Confluent, Apache Pulsar 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.
