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 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."
"One of the best features of Amazon Kinesis is the multi-partition."
"The integration capabilities of the product are good."
"Setting Amazon Kinesis up is quick and easy; it only takes a few minutes to configure the necessary settings and start using it."
"I find almost all features valuable, especially the timing and fast pace movement."
"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."
"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."
"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."
"It's the fastest solution on the market with low latency data on data transformations."
"The solution is better than average and some of the valuable features include efficiency and stability."
"The solution is very stable and reliable."
"As an open-source solution, using it is basically free."
"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
"We were charged high costs for the solution’s enhanced fan-out feature."
"Lacks first in, first out queuing."
"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."
"Amazon Kinesis involved a more complex setup and configuration than Azure Event Hub."
"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."
"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."
"I suggest integrating additional features, such as incorporating Amazon Pinpoint or Amazon Connect as bundled offerings, rather than deploying them as separate services."
"The solution itself could be easier to use."
"Integrating event-level streaming capabilities could be beneficial."
"It was resource-intensive, even for small-scale applications."
"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 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 cost and load-related optimizations are areas where the tool lacks and needs improvement."
"We would like to have the ability to do arbitrary stateful functions in Python."
"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 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.