Data modelling: Populate index page

Author: amotl

docs/start/modelling/fulltext.md

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+(model-fulltext)=
 # Full-text data
 
 CrateDB features **native full‑text search** powered by **Apache Lucene** and Okapi BM25 ranking, fully accessible via SQL. You can blend this seamlessly with other data types—JSON, time‑series, geospatial, vectors and more—all in a single SQL query platform.

docs/start/modelling/geospatial.md

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+(model-geospatial)=
 # Geospatial data
 
 CrateDB supports **real-time geospatial analytics at scale**, enabling you to store, query, and analyze 2D location-based data using standard SQL over two dedicated types: **GEO\_POINT** and **GEO\_SHAPE**. You can seamlessly combine spatial data with full-text, vector, JSON, or time-series in the same SQL queries.

docs/start/modelling/index.md

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+(modelling)=
+(data-modelling)=
 # Data modelling
 
+:::{div} sd-text-muted
 CrateDB provides a unified storage engine that supports different data types.
+:::
+
+:::::{grid} 2 3 3 3
+:padding: 0
+:class-container: installation-grid
+
+::::{grid-item-card} Relational data
+:link: model-relational
+:link-type: ref
+:link-alt: Relational data
+:padding: 3
+:text-align: center
+:class-card: sd-pt-3
+:class-body: sd-fs-1
+:class-title: sd-fs-6
+
+{fas}`table-list`
+::::
+
+::::{grid-item-card} JSON data
+:link: model-json
+:link-type: ref
+:link-alt: JSON data
+:padding: 3
+:text-align: center
+:class-card: sd-pt-3
+:class-body: sd-fs-1
+:class-title: sd-fs-6
+
+{fas}`file-lines`
+::::
+
+::::{grid-item-card} Timeseries data
+:link: model-timeseries
+:link-type: ref
+:link-alt: Timeseries data
+:padding: 3
+:text-align: center
+:class-card: sd-pt-3
+:class-body: sd-fs-1
+:class-title: sd-fs-6
+
+{fas}`timeline`
+::::
+
+::::{grid-item-card} Geospatial data
+:link: model-geospatial
+:link-type: ref
+:link-alt: Geospatial data
+:padding: 3
+:text-align: center
+:class-card: sd-pt-3
+:class-body: sd-fs-1
+:class-title: sd-fs-6
+
+{fas}`globe`
+::::
+
+::::{grid-item-card} Fulltext data
+:link: model-fulltext
+:link-type: ref
+:link-alt: Fulltext data
+:padding: 3
+:text-align: center
+:class-card: sd-pt-3
+:class-body: sd-fs-1
+:class-title: sd-fs-6
+
+{fas}`font`
+::::
+
+::::{grid-item-card} Vector data
+:link: model-vector
+:link-type: ref
+:link-alt: Vector data
+:padding: 3
+:text-align: center
+:class-card: sd-pt-3
+:class-body: sd-fs-1
+:class-title: sd-fs-6
+
+{fas}`lightbulb`
+::::
+
+:::::
+
+
 ```{toctree}
 :maxdepth: 1
+:hidden:
 
 relational
 json
@@ -12,10 +103,28 @@ fulltext
 vector
 ```
 
-Because CrateDB is a distributed OLAP database designed store large volumes
-of data, it needs a few special considerations on certain details.
+:::{rubric} Implementation notes
+:::
+
+Because CrateDB is a distributed analytical database (OLAP) designed to store
+large volumes of data, users need to consider certain details compared to
+traditonal RDBMS.
+
+
+:::{card} Primary key strategies
+:link: model-primary-key
+:link-type: ref
+CrateDB is built for horizontal scalability and high ingestion throughput.
++++
+To achieve this, operations must complete independently on each node—without
+central coordination. This design choice means CrateDB does not support
+traditional auto-incrementing primary key types like `SERIAL` in PostgreSQL
+or MySQL by default.
+:::
+
 ```{toctree}
 :maxdepth: 1
+:hidden:
 
 primary-key
 ```

docs/start/modelling/json.md

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+(model-json)=
 # JSON data
 
 CrateDB combines the flexibility of NoSQL document stores with the power of SQL. It enables you to store, query, and index **semi-structured JSON data** using **standard SQL**, making it an excellent choice for applications that handle diverse or evolving schemas.

docs/start/modelling/primary-key.md

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+(model-primary-key)=
 # Primary key strategies
 
 CrateDB is built for horizontal scalability and high ingestion throughput. To achieve this, operations must complete independently on each node—without central coordination. This design choice means CrateDB does **not** support traditional auto-incrementing primary key types like `SERIAL` in PostgreSQL or MySQL by default.

docs/start/modelling/relational.md

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+(model-relational)=
 # Relational data
 
 CrateDB is a **distributed SQL database** that offers rich **relational data modeling** with the flexibility of dynamic schemas and the scalability of NoSQL systems. It supports **primary keys,** **joins**, **aggregations**, and **subqueries**, just like traditional RDBMS systems—while also enabling hybrid use cases with time-series, geospatial, full-text, vector search, and semi-structured data.

docs/start/modelling/timeseries.md

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+(model-timeseries)=
 # Time series data
 
 CrateDB employs a relational representation for time‑series, enabling you to work with timestamped data using standard SQL, while also seamlessly combining with document and context data.

docs/start/modelling/vector.md

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+(model-vector)=
 # Vector data
 
 CrateDB natively supports **vector embeddings** for efficient **similarity search** using **approximate nearest neighbor (ANN)** algorithms. This makes it a powerful engine for building AI-powered applications involving semantic search, recommendations, anomaly detection, and multimodal analytics—all in the simplicity of SQL.