usage-analytics-architecture

A canonical usage analytics event model standardizes tracking across frontend, backend, and mobile streams into a consistent schema. This enables reliable DAU/MAU, funnel, and retention analysis by enforcing EventID deduplication and keeping PII out of event properties.

Build a usage data pipeline using a non-blocking fire-and-forget emission path to prevent application latency. The pipeline batches usage events and delivers them as robust sinks to data warehouses like BigQuery, Redshift, or ClickHouse for downstream analysis.

Yes, the usage analytics architecture supports both real-time and batch processing. It handles multi-tenant telemetry scaling across SaaS platforms, streaming sinks, and batched writes to deliver usage data efficiently to your designated data warehouse.

Track feature adoption by enforcing a non-blocking fire-and-forget emission path for analytics events. This architectural choice ensures event tracking for adoption and billing signals operates reliably without degrading frontend, backend, or mobile application performance.

PII must be avoided in usage analytics event properties to maintain data privacy and compliance within your SaaS pipeline. The canonical AnalyticsEvent schema enforces this constraint while ensuring reliable product analytics and consistent cross-platform querying.

Yes, you can use ClickHouse as a data warehouse sink. The end-to-end usage data pipeline supports delivery to BigQuery, Redshift, and ClickHouse, enabling consistent querying for DAU/MAU, funnel analysis, and retention tracking across your SaaS platform.