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Multimodal Data Analytics

CityDrive Intelligence records video of every drive. Background processing tools split the video stream into keyframe images, extracting rich multimodal information from each image and storing it by video_id. These signals include relational metadata, JSON manifests, behavior tags, vector embeddings, and GPS traces.

This guide set shows how TiDB Cloud Lake 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.

Multimodal Data Analytics

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