From JSON Webhooks to Kafka-Fed Parquet: A Measured Migration for Event-Driven BI

This InSight addresses four practical domains in event-driven analytics engineering. On data lake partition strategy, it establishes that partition layout, not data volume, is the primary driver of ingestion cost, and shows how switching from per-entity to per-date folder structures removes the discovery bottleneck entirely. On Auto Loader optimisation, it documents how Databricks Auto Loader discovery degrades non-linearly at scale under per-order layouts, and what a layout-only change delivers in measured production conditions. On Kafka-to-Parquet pipeline design, it covers how to replace a JSON-webhook ingest path with a Kafka-fed, columnar-landing architecture using a Rust consumer, including schema derivation, buffering, date-splitting, and delivery semantics. On BI SLA reliability, it argues that producer-side decisions about format, layout, and transport are the decisions that determine the predictability, recoverability, and query performance of a BI lake for as long as the data lives.