DataScale: Real-time Analytics Platform
We built a streaming analytics platform processing millions of events per second with sub-second dashboards for operations and product analytics. The architecture enables real-time transforms, late-arriving data handling, and historical replay for root-cause analysis, while keeping infra costs predictable.
Challenge
Legacy batch ETL pipelines caused stale data and heavy operational overhead for analytics consumers. Multiple competing data marts created divergence, breaking trust in the numbers and slowing decisions.
Background
Multiple batch ETL jobs produced conflicting reports. Teams duplicated effort maintaining separate data marts. Analytics lag meant incidents were discovered hours later, and product teams could not measure experiments in real time.
Solution
Kafka-based ingestion, Flink streaming transforms, and ClickHouse for OLAP queries, exposed via a Next.js dashboard with role-based access. We established a unified event schema, compaction and retention strategies, and a governance layer that tracks lineage and quality, with automated monitors and anomaly alerts.
Implementation
We designed a unified event schema, set up Kafka topics per domain, implemented Flink pipelines for enrichment and joins, and wrote to ClickHouse for fast OLAP queries. We added lineage, quality monitors, and a Next.js dashboard with RBAC, alerts, and drilldowns. A playbook guided onboarding of new data sources.
Impact
- Sub-second slice-and-dice analytics
- Reduced infra costs via columnar storage
- Self-service dashboards for teams
- Faster incident response via real-time alerts
- Improved metric consistency across org
- Streamlined governance and lineage visibility
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