EventStream

We migrated from self-managed Kafka to EventStream and eliminated about 40 hours per month of operational toil. No more broker upgrades, partition reassignment headaches, or Zookeeper maintenance. The Kafka protocol compatibility meant zero code changes.

EventStream's SQL-based stream processing is surprisingly powerful. We use it for real-time event enrichment, windowed aggregations, and filtering -- all without deploying Kafka Streams or Flink applications. This eliminated two separate infrastructure components from our architecture.

Good managed Kafka alternative. The web console provides excellent visibility into topic metrics, consumer group lag, and cluster health. The dead letter queue management is a nice feature we did not have with self-managed Kafka.

EventStream made event-driven architecture accessible for our small engineering team. Previously, the operational overhead of Kafka deterred us. We process about 10 million events per day across 50 topics and the platform has been rock-solid.

We use EventStream as the backbone of our real-time analytics pipeline. Events from our web application flow through EventStream to our stream processing jobs and into ClickHouse for real-time dashboards. The sub-10ms publish latency is critical for our use case.

EventStream handles our multi-tenant SaaS event architecture well. We use topic-level ACLs to ensure data isolation between tenants, and the auto-scaling handles variable load across tenants gracefully.

Decent managed Kafka platform. The basic streaming features work well. However, I found the stream processing capabilities somewhat limited compared to dedicated tools like Apache Flink. Good for simpler use cases.

EventStream powers our real-time recommendation engine. We publish user interaction events, process them through stream processing for feature computation, and feed the results into our ML model. The end-to-end latency is under 500ms. Zero data loss events in eight months.