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."From my experience, one of the most valuable features is the ability to track silent events on endpoints. Previously, these events might have gone unnoticed, but now we can access them within the product range. For example, if a customer reports that their calls are not reaching the portal files, we can use this feature to troubleshoot and optimize the system."
"The solution works well in rather sizable environments."
"The management and analytics are valuable features."
"I find almost all features valuable, especially the timing and fast pace movement."
"The scalability is pretty good."
"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's technical support is flawless."
"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."
"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."
"The solution is very stable and reliable."
"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."
"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."
"It's the fastest solution on the market with low latency data on data transformations."
"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."
"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 can be expensive, especially when dealing with large volumes of data."
"For me, especially with video streams, there's sometimes a kind of delay when the data has to be pumped to other services. This delay could be improved in Kinesis, or especially the Kinesis Video Streams, which is being used for different use cases for Amazon Connect. With that improvement, a lot of other use cases of Amazon Connect integrating with third-party analytic tools would be easier."
"We were charged high costs for the solution’s enhanced fan-out feature."
"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."
"Amazon Kinesis should improve its limits."
"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."
"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."
"The solution has a two-minute maximum time delay for live streaming, which could be reduced."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"Integrating event-level streaming capabilities could be beneficial."
"The initial setup is quite complex."
"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."
"In terms of improvement, the UI could be better."
"It was resource-intensive, even for small-scale applications."
"The solution itself could be easier to use."
"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."
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.
