Amazon Kinesis and Amazon MSK compete in the data streaming and processing category. Amazon Kinesis appears to hold the upper hand due to its simplicity and ease of integration with AWS services, though Amazon MSK offers more control for larger Apache Kafka workloads.
Features: Amazon Kinesis offers simplicity as a fully managed service with real-time data processing and robust AWS service integration. It handles real-time streaming data with low latency and provides seamless Lambda integration. Conversely, Amazon MSK is noted for its ability to manage larger Kafka workloads, offering more control over the Kafka environment. It excels in handling substantial data volumes efficiently and provides cost-effective solutions for intensive Kafka applications.
Room for Improvement: Amazon Kinesis users suggest improving default throughput limits and offering more intuitive sharding capabilities. Enhancements in visibility within data streams and better non-AWS service integration are also desirable. In contrast, Amazon MSK is criticized for its complex initial setup and higher costs for large-scale deployments. Enhancements in integration flexibility and schema support are needed, along with documentation improvements.
Ease of Deployment and Customer Service: Amazon Kinesis allows swift deployment in public cloud environments, although technical support experiences vary. Amazon MSK supports both public and hybrid cloud configurations but may require more complex deployment steps. Despite this, MSK's support experiences are generally favorable with quick response times.
Pricing and ROI: Amazon Kinesis is seen as moderately priced, offering significant cost savings and positive ROI for integrated AWS use cases. Amazon MSK, while competitively priced for larger workloads, is noted for higher costs in smaller use cases. However, it can provide better long-term value for intensive workloads due to its resource optimization capabilities.
With Lambda, there is no need for data transfer charges, which is beneficial for less frequent workloads.
We receive prompt support from AWS solution architects or TAMs.
Amazon's support is excellent.
Amazon Kinesis provides auto-scaling with streams that handle large volumes well.
The functionality for scaling comes out of the box and is very effective.
Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes.
The increase in cloud costs by 50% to 60% does not justify the savings.
Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.
Once we started using Kafka, our cloud costs rose by 50% to 60%.
Lambda's scalability, seamless integration with other AWS services, and support for multiple programming languages are very beneficial.
The scalability and usability are quite remarkable.
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and respond instantly instead of having to wait until all your data is collected before the processing can begin.
Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that enables you to build and run applications that use Apache Kafka to process streaming data. Amazon MSK provides the control-plane operations, such as those for creating, updating, and deleting clusters.
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