8M+
Events Processed Daily
<800ms
Query Response Time (p95)
3 years
Data Retention
500+
Concurrent Dashboard Users
60%
Storage Efficiency Gain

Problem

The client's existing analytics stack could not keep pace with growing data volumes. Batch processing introduced 4–6 hour delays in reporting, making real-time operational decisions impossible. Dashboard queries on large datasets timed out regularly, and the storage costs were growing unsustainably.

Solution

We implemented a streaming analytics pipeline using ClickHouse as the core analytical database, fed by Kafka-based event ingestion. A custom query optimization layer provides sub-second responses for complex aggregations across billions of rows. The system includes automated data lifecycle management to control storage costs.

Technology Used

ClickHouseApache KafkaKubernetesGoRedisGrafanaDocker

Impact

Reduced reporting latency from 6 hours to under 5 seconds
Enabled real-time operational dashboards for 500+ concurrent users
Decreased storage costs by 60% through columnar compression and lifecycle management
Eliminated dashboard query timeouts entirely

Architecture Highlights

ClickHouse columnar storage with custom materialized views for common query patterns
Kafka-based streaming ingestion with exactly-once processing guarantees
Tiered storage strategy with automatic data migration based on access patterns
Custom query routing layer that directs queries to optimized replicas

Lessons Learned

Columnar databases fundamentally change what is possible in analytics query performance
Materialized views should be designed around actual usage patterns, not anticipated ones
Data lifecycle automation is essential for controlling costs at scale