Our primary use case is serving as a real-time data backbone within our cloud-native architecture. Specifically, we rely on AutoMQ Cloud to handle high-throughput user behavior logs and order transaction data. Previously, when using traditional Apache Kafka, storage scaling and configuring cross-AZ high availability were constant pain points for our DevOps team. After migrating to AutoMQ Cloud, the key benefit for us is its compute-storage separation architecture. Now, during traffic spikes (like major sales events), we can independently scale up computing resources to handle write pressure, while the cost of storing massive amounts of historical data has dropped significantly thanks to S3 integration. Simply put, it has freed us from the heavy operational burden of managing Kafka, allowing us to focus much more on developing core business logic.
Our primary use case is serving as a real-time data backbone within our cloud-native architecture. Specifically, we rely on AutoMQ Cloud to handle high-throughput user behavior logs and order transaction data. Previously, when using traditional Apache Kafka, storage scaling and configuring cross-AZ high availability were constant pain points for our DevOps team. After migrating to AutoMQ Cloud, the key benefit for us is its compute-storage separation architecture. Now, during traffic spikes (like major sales events), we can independently scale up computing resources to handle write pressure, while the cost of storing massive amounts of historical data has dropped significantly thanks to S3 integration. Simply put, it has freed us from the heavy operational burden of managing Kafka, allowing us to focus much more on developing core business logic.