We performed a comparison between Amazon Kinesis and Apache Pulsar based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Confluent and others in Streaming Analytics."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."
"The scalability is pretty good."
"Setting Amazon Kinesis up is quick and easy; it only takes a few minutes to configure the necessary settings and start using it."
"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 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 works well in rather sizable environments."
"One of the best features of Amazon Kinesis is the multi-partition."
"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."
"The solution operates as a classic message broker but also as a streaming platform."
"If there were better documentation on optimal sharding strategies then it would be helpful."
"I suggest integrating additional features, such as incorporating Amazon Pinpoint or Amazon Connect as bundled offerings, rather than deploying them as separate services."
"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."
"Could include features that make it easier to scale."
"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."
"One thing that would be nice would be a policy for increasing the number of Kinesis streams because that's the one thing that's constant. You can change it in real time, but somebody has to change it, or you have to set some kind of meter. So, auto-scaling of adding and removing streams would be nice."
"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 price is not much cheaper. So, there is room for improvement in the pricing."
"Documentation is poor because much of it is in Chinese with no English translation."
Amazon Kinesis is ranked 2nd in Streaming Analytics with 21 reviews while Apache Pulsar is ranked 12th in Streaming Analytics with 1 review. Amazon Kinesis is rated 8.0, while Apache Pulsar 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 Pulsar writes "The solution can mimic other APIs without changing a line of code". Amazon Kinesis is most compared with Azure Stream Analytics, Apache Flink, Amazon MSK, Confluent and Spring Cloud Data Flow, whereas Apache Pulsar is most compared with Apache Flink, Apache Spark Streaming, Amazon MSK, Azure Stream Analytics and Google Cloud Dataflow.
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