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.
With Lambda, there is no need for data transfer charges, which is beneficial for less frequent workloads.
Returns depend on the application you deploy and the amount of benefits you are getting, which depends on how many applications you are deploying, what are the sorts of applications, and what are the requirements.
We receive prompt support from AWS solution architects or TAMs.
I would rate them eight if 10 was the best and one was the worst.
Amazon Kinesis provides auto-scaling with streams that handle large volumes well.
Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes.
Observability and monitoring are areas that could be enhanced.
Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.
Lambda's scalability, seamless integration with other AWS services, and support for multiple programming languages are very beneficial.
These features are important due to scalability and resiliency.
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.
Apache Kafka on Confluent Cloud provides real-time data streaming with seamless integration, enhanced scalability, and efficient data processing, recognized for its real-time architecture, ease of use, and reliable multi-cloud operations while effectively managing large data volumes.
Apache Kafka on Confluent Cloud is designed to handle large-scale data operations across different cloud environments. It supports real-time data streaming, crucial for applications in transaction processing, change data capture, microservices, and enterprise data movement. Users benefit from features like schema registry and error handling, which ensure efficient and reliable operations. While the platform offers extensive connector support and reduced maintenance, there are areas requiring improvement, including better data analysis features, PyTRAN CDC integration, and cost-effective access to premium connectors. Migrating with Kubernetes and managing message states are areas for development as well. Despite these challenges, it remains a robust option for organizations seeking to distribute data effectively for analytics and real-time systems across industries like retail and finance.
What are the key features of Apache Kafka on Confluent Cloud?In industries like retail and finance, Apache Kafka on Confluent Cloud is implemented to manage real-time location tracking, event-driven systems, and enterprise-level data distribution. It aids in operations that require robust data streaming, such as CDC, log processing, and analytics data distribution, providing a significant edge in data management and operational efficiency.
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