Real-Time Analytics Pipeline

The client's e-commerce platform relied on overnight batch jobs to populate their analytics dashboards. This 24-hour data lag meant inventory managers were making decisions on stale data, causing stockouts during flash sales and over-ordering in slow periods. The business was losing an estimated $2M annually in preventable inventory errors.

Designed a Lambda Architecture that processes both real-time and historical data streams, ensuring consistency while enabling sub-minute analytics.

• Implemented Apache Kafka as the central event streaming backbone, ingesting 50,000+ events per second from the transaction layer

• Built Apache Spark Structured Streaming jobs to process and enrich events in micro-batches of 30 seconds

• Designed a Snowflake schema with clustering keys optimized for the analytics query access patterns

• Created dbt transformation models for the serving layer, cleanly separating raw ingestion from business logic

• Deployed the entire pipeline on AWS EKS with automated HPA scaling policies tied to Kafka consumer lag

• Reduced analytics data latency from 24 hours to under 3 minutes

• Inventory accuracy improved 34%, directly reducing stockout events by 41%

• Dashboard query performance improved 8× due to optimized Snowflake clustering keys

• Infrastructure costs reduced 22% by replacing over-provisioned legacy servers with auto-scaling containers