Kafka (Message Queue)

It is a high-throughput and high-availability message middleware service developed based on the open source Apache Kafka, which is suitable for building real-time data pipelines, streaming data processing and other scenarios. Kafka (Message Queue) provides a fully managed service, which eliminates users’ need for O&M deployment and maintenance, making the service more secure and reliable.

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Product Advantages
Product functions
Application scenarios
solution

Product Advantages

Out-of-the-box
It is 100% compatible with the open source community Kafka, the business code does not need to be transformed, and rapid cloud migration can be achieved.
Fully managed services
Fully managed deployment and operation and maintenance services allow users to only need to focus on the business itself, making the service more secure and reliable.
High performance
Professionally optimized Kafka service with high throughput, low latency, and strong message accumulation capabilities.
Data security
It supports SASL_SSL user identity authentication and channel encryption transmission mechanism to ensure that data is not stolen or tampered with.

Product functions

Open source compatibility
It is 100% compatible with the open source community Kafka and fully compatible with the Apache Kafka open source client.
Massive message accumulation
In the case of massive message accumulation, the high throughput of message queues is not affected.
High concurrency
The concurrency of a single queue can exceed 100,000 TPS (messages processed per second).
High reliability
Clustered deployment, multi-replica data redundancy, service availability up to 99.9%.
Data security
It supports SASL_SSL user identity authentication and channel encryption transmission mechanism to ensure that data is not stolen or tampered with during transmission.

Application scenarios

Enterprise data bus
Internet of Things applications

Real-time data reliably reduces coupling between applications. The data bus is built through Kafka, and the same data generated can be distributed to different dedicated systems to ensure the real-time reliability of data, reduce the coupling between business applications, and solve the problem of abnormal coupling services caused by single service failure.
For the data storage and distribution channel system, Kafka is used as the data channel and stream computing engines such as Spark and Flink are used to realize real-time computing and analysis of IoT data, and store or distribute the computing results to the system.

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