Templates/High-frequency Analytics Pipeline

High-frequency Analytics Pipeline

Data EngineeringAdvancedData platform

High-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 teams running clickstream analytics

8 nodes8 connectionsStream + batch splitColumnar queriesHigh-volume ingest

Scope Notes

Intended for Product teams running clickstream analytics at Advanced complexity and Data platform scope. Treat the graph as a starting shape rather than a fixed stack: keep the topology and the signals below, then swap providers to match your platform.

Stream + batch splitColumnar queriesHigh-volume ingest

Review Checklist

0 of 12 reviewed

Use Case

Product analytics, clickstream processing, real-time dashboards, data warehousing

Best Fit Scenarios

  • High-volume clickstream ingestion
  • Real-time plus historical rollups
  • Sub-second dashboard queries

Stack Breakdown

SDK / PixelsCollectorKafkaFlinkSparkClickHouse

Architecture Layers

1Collection
2Ingestion & buffering
3Stream processing
4Batch processing
5Storage
6Visualisation

Components by Category

frontend

SDK / PixelsGrafana

backend

CollectorFlinkSpark

async

Kafka

database

ClickHouse

infra

S3 Data Lake

Why This Topology Works

The collector normalises SDK and pixel traffic into Kafka, Flink aggregates in real time for dashboards, and Spark runs historical rollups into the lake and ClickHouse. ClickHouse serves sub-second analytical queries to Grafana.

Scaling Notes

Kafka partitions by event type, Flink holds the streaming aggregation, and Spark jobs scale with data volume. ClickHouse uses distributed tables for large-scale queries while S3 Data Lake keeps the raw archive.

Observability

Monitor Kafka consumer lag, Flink job health, Spark job SLA and ClickHouse query P99. Grafana dashboards are served from ClickHouse, so freshness problems surface as stale panels.

Typical Bottlenecks

  • Kafka partition skew and consumer lag
  • ClickHouse merge pressure on high-cardinality queries
  • Collector ingest spikes from public SDKs

Async Flow and Reliability

Events are ingested and buffered asynchronously; real-time aggregation and batch rollups run as independent consumers. Nothing on the collection path waits for processing.

Upgrade Path

Add schema validation with a quarantine path right after the collector, authenticate and rate-limit the ingest endpoint, and archive Kafka to S3 before Spark reads it.