High-frequency Analytics Pipeline
Data EngineeringProductionTeam to orgHigh-throughput analytics pipeline ingesting clickstream events via Kafka, processing in real-time with Flink, archiving with Spark to S3, and querying with ClickHouse for live dashboards.
Recommended for: Product analytics
Use Case
Product analytics, clickstream processing, real-time dashboards, data warehousing
Best Fit Scenarios
- Product analytics
- Clickstream processing
- Real-time dashboards
Stack Breakdown
Architecture Layers
Components by Category
frontend
backend
async
database
infra
Why This Topology Works
Kafka decouples collection from processing. Flink handles real-time aggregations for dashboards while Spark runs historical rollups. ClickHouse serves sub-second analytical queries.
Scaling Notes
Kafka partitions by event type. Flink checkpoints to S3. Spark jobs scale with data volume. ClickHouse uses distributed tables for petabyte-scale queries.
Observability
Monitor Kafka consumer lag, Flink checkpoint duration, Spark job SLA, and ClickHouse query P99. Grafana dashboards self-monitor via ClickHouse.
Typical Bottlenecks
- Frontend rendering and bundle delivery under peak traffic
- Service latency and timeout behavior on critical routes
- Queue lag, retry storms, and DLQ growth during incidents
Async Flow and Reliability
User-facing operations remain synchronous while long-running work moves through queues or streams. Workers consume jobs independently with retry and failure isolation, improving resilience under burst load.
Upgrade Path
Split high-churn domains into dedicated services, then introduce stronger queue policies and SLO-driven monitoring.
Operating Envelope
Complexity is marked as Production with an intended scope of Team to org. Use this as a planning baseline before adapting the template to your reliability and team constraints.