Data without context is just noise. In this post, I share how I built a BI dashboard that transforms raw transaction data into actionable insights for a Nairobi-based fintech company.
The architecture uses a Lambda approach: daily Spark batch jobs for historical aggregations, Kafka streams for real-time metrics, and PostgreSQL with Redis for the serving layer.
After deploying the dashboard, the operations team reduced their reporting time from 3 hours per day to 15 minutes. More importantly, the fraud team identified a pattern that saved the company $50,000 in the first month alone.
The lesson: the value of data engineering is not in the technology — it is in the decisions it enables.