Analysing historical and live data with ksqlDB and Elastic Cloud

This post details building data pipelines by transforming raw CSV data from car park occupancy into a structured format using ksqlDB, applying schemas, setting message keys for partitioning, adding source fields for lineage, deriving and storing timestamps, creating nested location structs, and calculating new fields. The processed data is then streamed to Elasticsearch for analytics and visualization with Kibana.