High-frequency Analytics Pipeline
Data EngineeringStream processing pipeline for clickstream, metrics, and real-time dashboards
8 nodes8 connections
Use Case
Product analytics, clickstream processing, real-time dashboards, data warehousing
Stack Breakdown
SDKKafkaFlinkSparkClickHouseGrafana
Architecture Layers
1Collection Layer
2Ingestion & Buffering
3Stream Processing
4Batch Processing
5Storage & Visualization
Components by Category
frontend
SDK / PixelsGrafana
backend
CollectorFlinkSpark
async
Kafka
database
ClickHouse
infra
S3 Data Lake
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.