Real-time event pipelines break down in one of two places: events don't make it through the ingestion layer reliably, or they never become fast queries in the platform. These are separate problems that require different tools, and solving only one of them is how teams end up with expensive, fragile stacks.

Without a durable, streaming backbone, distributed data producers and data consumers become tightly coupled. Producers are the applications that generate and send events into Kafka topics, while consumers are the services that read those events. Producers write directly to queues, databases, or custom services, and when a consumer goes down or lags, the producer either blocks or starts dropping events.

Furthermore, PostgreSQL and similar OLTP Public Cloud databases are designed for row-level operations: inserts, updates, deletes. They are not designed to scan billions of rows, aggregate across multiple dimensions, or update dashboards every minute. As event volume grows, queries that used to return in 200 ms start taking seconds, then tens of seconds, then minutes. The system becomes a bottleneck for product decisions.

When the app pipeline is slow or unreliable, the consequences are measurable. Fraud detection response times stretch from seconds to minutes, which in FinTech translates directly into financial loss and reputational exposure. Slow reporting cycles block product decisions and force teams to rely on snapshots that are already stale by the time they are reviewed. To compensate, teams start patching the stack. Custom ETL jobs, hand-assembled message queues, and reporting views accumulate into an ad-hoc pipeline that must be maintained manually. The real cost is not just the immediate latency. It is the operational drag, the technical debt, and the fact that the team cannot iterate on real-time features because they are busy keeping the existing pipeline alive.