Built an enterprise streaming data pipeline processing 2M+ events per hour with sub-second latency for real-time business intelligence.
The Challenge
Legacy ETL batch jobs were causing 6-hour data lag for a business intelligence platform serving financial clients. Dashboards showed yesterday's data while the business needed live insights.
Any data loss during failover was unacceptable due to compliance requirements. The existing system had no replay capability and no fault tolerance.
Our Approach
We designed the new architecture around Apache Kafka as the central event bus, replacing the batch ETL entirely. Each data source got a dedicated producer; each BI consumer subscribed independently, allowing independent scaling.
We ran both systems in parallel for 30 days during cutover, validating data parity before decommissioning the legacy pipeline.
The Solution
A Kafka-based streaming pipeline processing 2M+ events per hour with full replay capability, consumer group isolation per BI use case, Redis caching for hot data, and a Grafana observability stack with automated alerting on lag, throughput and error rates.
Full data at rest and in transit encryption for compliance. AWS MSK for managed Kafka with multi-AZ replication.
The Results
Data latency dropped from 6 hours to under 1 second from day one of cutover. Zero data loss events recorded in 12 months of operation. The compliance team signed off on the new architecture within the first audit cycle.
These guys don't just code — they architect. Our data pipeline now handles 2M+ events an hour and the entire system is rock solid. Long-term partner for us.
Key Learnings
Parallel Running is Non-Negotiable
Running old and new systems simultaneously for 30 days with data parity checks is the only safe way to migrate a live financial data pipeline.
Observability is Architecture
Building Grafana dashboards and lag alerts as part of the core system — not as an afterthought — is what gives operations teams the confidence to trust the pipeline.
Consumer Isolation Pays Dividends
Separate consumer groups per BI use case allowed independent scaling and meant one slow consumer could never block another.