We performed a comparison between Amazon MSK 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."Overall, it is very cost-effective based on the workflow."
"MSK has a private network that's an out-of-box feature."
"It is a stable product."
"It offers good stability."
"The most valuable feature of Amazon MSK is the integration."
"Amazon MSK has significantly improved our organization by building seamless integration between systems."
"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 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."
"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."
"It's the fastest solution on the market with low latency data on data transformations."
"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 should be more flexible, integration-wise."
"It would be really helpful if Amazon MSK could provide a single installation that covers all the servers."
"It does not autoscale. Because if you do keep it manually when you add a note to the cluster and then you register it, then it is scalable, but the fact that you have to go and do it, I think, makes it, again, a bit of some operational overhead when managing the cluster."
"The product's schema support needs enhancement. It will help enhance integration with many kinds of languages of programming languages, especially for environments using languages like .NET."
"Amazon MSK could improve on the features they offer. They are still lagging behind Confluence."
"The configuration seems a little complex and the documentation on the product is not available."
"The initial setup is quite complex."
"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."
"It was resource-intensive, even for small-scale applications."
"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."
"The solution itself could be easier to use."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"In terms of improvement, the UI could be better."
Amazon MSK is ranked 6th in Streaming Analytics with 5 reviews while Apache Spark Streaming is ranked 8th in Streaming Analytics with 6 reviews. Amazon MSK is rated 7.2, while Apache Spark Streaming is rated 8.0. The top reviewer of Amazon MSK writes " A stable data streaming solution for message queue integration ". On the other hand, the top reviewer of Apache Spark Streaming writes "Easy deployment as a cluster and good documentation". Amazon MSK is most compared with Confluent, Amazon Kinesis, Azure Stream Analytics, Google Cloud Dataflow and Apache Flink, whereas Apache Spark Streaming is most compared with Amazon Kinesis, Azure Stream Analytics, Spring Cloud Data Flow and Confluent. See our Amazon MSK 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.