Live Sales and Inventory Analytics for Online Storefronts

Added Jun 2025 3 design docs

In e-commerce, an hour of delayed insight is real money: a viral product sells out before restocking triggers, or a broken checkout quietly bleeds conversions overnight. Store operators need live visibility into sales, inventory, and customer behavior, not yesterday's batch report delivered after the moment has passed. The intern builds a live analytics suite where Kafka ingests real-time streams of orders, inventory changes, and browsing events from multiple data sources. Python services consume the streams, with pandas powering the aggregation and anomaly detection that flags unusual patterns, whether a sudden sales spike, an approaching stockout, or a conversion drop, as they emerge rather than after the fact. PostgreSQL stores the processed metrics, and a Next.js frontend delivers custom dashboards where operators watch live sales, drill into product-level performance, and configure exactly the alerts that matter to their store. The project covers the full streaming analytics stack in a commercial setting: designing Kafka topics for retail events, building consumers that stay correct under bursty traffic, and turning raw streams into dashboards that drive same-day business decisions.

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