Unified Workloads

CityDrive Intelligence records every dash-cam drive, splits it into frames, and stores multiple signals per video_id: relational metadata, JSON manifests, behaviour tags, embeddings, and GPS traces. This guide set shows how Databend keeps all those workloads in one warehouse—no copy jobs, no extra search cluster.

Guide	What it covers
SQL Analytics	Base tables, filters, joins, windows, aggregating indexes
JSON & Search	Load frame_metadata_catalog, run Elasticsearch QUERY(), link bitmap tags
Vector Search	Persist embeddings, run cosine search, join risk metrics
Geo Analytics	Use GEOMETRY, distance/polygon filters, traffic-light joins
Lakehouse ETL	Stage once, COPY INTO shared tables, add streams/tasks

Walk through them in order to see how the same identifiers flow from classic SQL to text search, vector, geo, and ETL—everything grounded in a single CityDrive scenario.