Navigation: Move reference links to bottom in learning-path sections

amotl · amotl · commit 94dfc739b83a · 2025-10-07T19:39:11.000+02:00
diff --git a/docs/start/query/ad-hoc.md b/docs/start/query/ad-hoc.md
@@ -5,13 +5,6 @@
 Support highly dynamic ad-hoc querying even on large-scale, real-time datasets.
 :::
 
-:::::{grid}
-:padding: 0
-
-::::{grid-item}
-:class: rubric-slimmer
-:columns: auto 9 9 9
-
 :::{rubric} Introduction
 :::
 
@@ -57,35 +50,6 @@ They are unpredictable by nature—and CrateDB is designed to handle exactly tha
 - Avoid maintaining rigid ETL pipelines or OLAP cubes
 
 
-::::
-
-::::{grid-item}
-:class: rubric-slim
-:columns: auto 3 3 3
-
-:::{rubric} Related Features
-:::
-- {ref}`object`
-- {ref}`fts`
-- {ref}`timeseries`
-
-:::{rubric} Reference Documentation
-:::
-- {ref}`CrateDB SQL Reference <crate-reference:sql>`
-- {ref}`CrateDB Console <crate-crash:index>`
-- {ref}`crate-reference:data-types-objects`
-- {ref}`crate-reference:fulltext-indices`
-
-:::{rubric} Learn more
-:::
-- [Exploratory Time Series Data Analysis]
-- [Academy: Time Series Query Optimization]
-- [CrateDB Scalability Benchmark: Query Throughput]
-::::
-
-:::::
-
-
 ## Common Query Patterns
 
 ::::{grid} 2
@@ -242,6 +206,38 @@ Learn more about how to use ad-hoc queries effectively.
 | Full-text & filter                    | Combine keyword search with structured queries                                       | {ref}`fts` <br> {ref}`crate-reference:fulltext-indices`      |
 
 
+## Further reading
+
+:::::{grid} 1 3 3 3
+:margin: 4 4 0 0
+:padding: 0
+:gutter: 2
+
+::::{grid-item-card} {material-outlined}`article;1.5em` Documentation
+:columns: 3
+- {ref}`SQL reference <crate-reference:sql>`
+- {ref}`crate-reference:data-types-objects`
+- {ref}`crate-reference:fulltext-indices`
+::::
+
+::::{grid-item-card} {material-outlined}`link;1.5em` Related
+:columns: 3
+- {ref}`object`
+- {ref}`fts`
+- {ref}`timeseries`
+- {ref}`CrateDB Console <crate-crash:index>`
+::::
+
+::::{grid-item-card} {material-outlined}`read_more;1.5em` Read more
+:columns: 6
+- [Exploratory Time Series Data Analysis]
+- [Academy: Time Series Query Optimization]
+- [CrateDB Scalability Benchmark: Query Throughput]
+::::
+
+:::::
+
+
 [Academy: Time Series Query Optimization]: https://cratedb.com/academy/time-series/time-series-data-manipulation-and-visualization/time-series-query-optimization
 [CrateDB Scalability Benchmark: Query Throughput]: https://cratedb.com/blog/cratedb-scalability-benchmark-query-throughput
 [Exploratory Time Series Data Analysis]: https://cratedb.com/data-model/time-series/exploratory-data-analysis
diff --git a/docs/start/query/aggregations.md b/docs/start/query/aggregations.md
@@ -6,13 +6,6 @@
 High-performance aggregations on massive volumes of data using SQL.
 :::
 
-:::::{grid}
-:padding: 0
-
-::::{grid-item}
-:class: rubric-slimmer
-:columns: auto 9 9 9
-
 :::{rubric} Introduction
 :::
 
@@ -37,34 +30,6 @@ Whether you are monitoring sensor networks, analyzing customer behavior, or powe
 | Aggregations on any data type | Structured, JSON, full-text, geospatial, or vector                       |
 | Smart indexing                | Built-in indexing and configuration options that can boost performance   |
 
-::::
-
-::::{grid-item}
-:class: rubric-slim
-:columns: auto 3 3 3
-
-:::{rubric} Documentation
-:::
-- {ref}`crate-reference:aggregation`
-- {ref}`performance-select`
-
-:::{rubric} Integrations
-:::
-- {ref}`grafana`
-- {ref}`metabase`
-- {ref}`powerbi`
-- {ref}`superset`
-- {ref}`tableau`
-
-:::{rubric} See also
-:::
-- {ref}`analytics`
-- [Hands-on: Aggregating and Grouping Data]
-- [Real-Time Analytics Primer]
-::::
-
-:::::
-
 
 ## Supported Aggregation Functions
 
@@ -217,5 +182,37 @@ To learn about the full set of integrations, please visit the
 documentation at {ref}`bi` and {ref}`visualization`.
 
 
-[Hands-on: Aggregating and Grouping Data]: https://cratedb.com/academy/fundamentals/working-with-data-in-cratedb/hands-on-aggregating-and-grouping-data
-[Real-Time Analytics Primer]: https://cratedb.com/real-time-analytics/definition
+## Further reading
+
+:::::{grid} 1 3 3 3
+:margin: 4 4 0 0
+:padding: 0
+:gutter: 2
+
+::::{grid-item-card} {material-outlined}`article;1.5em` Documentation
+:columns: 3
+- {ref}`crate-reference:aggregation`
+- {ref}`performance-select`
+::::
+
+::::{grid-item-card} {material-outlined}`integration_instructions;1.5em` Integrations
+:columns: 3
+- {ref}`grafana`
+- {ref}`metabase`
+- {ref}`powerbi`
+- {ref}`superset`
+- {ref}`tableau`
+::::
+
+::::{grid-item-card} {material-outlined}`read_more;1.5em` Read more
+:columns: 6
+- Learn: [Real-time analytics primer]
+- Learn: [Hands-on: Aggregating and grouping data]
+- Solution: {ref}`analytics`
+::::
+
+:::::
+
+
+[Hands-on: Aggregating and grouping data]: https://cratedb.com/academy/fundamentals/working-with-data-in-cratedb/hands-on-aggregating-and-grouping-data
+[Real-time analytics primer]: https://cratedb.com/real-time-analytics/definition
diff --git a/docs/start/query/ai-integration.md b/docs/start/query/ai-integration.md
@@ -1,13 +1,6 @@
 (ai-integration)=
 # AI integration
 
-:::::{grid}
-:padding: 0
-
-::::{grid-item}
-:class: rubric-slimmer
-:columns: auto 9 9 9
-
 :::{rubric} Introduction
 :::
 
@@ -33,36 +26,6 @@ Whether you're training models, running batch or real-time inference, or integra
 | Monitoring          | Track model performance, drift, or input quality      |
 | Data Collection     | Capture telemetry, events, logs, and raw user data    |
 
-::::
-
-::::{grid-item}
-:class: rubric-slim
-:columns: auto 3 3 3
-
-:::{rubric} Related
-:::
-- {ref}`vector-search`
-- {ref}`hybrid-search`
-- {ref}`Machine Learning <ml>`
-
-:::{rubric} Integrations
-:::
-- {ref}`langchain`
-- {ref}`llamaindex`
-- {ref}`mindsdb`
-- {ref}`mlflow`
-- {ref}`pycaret`
-
-:::{rubric} See also
-:::
-- [Blog: Vector support and KNN search]
-- {ref}`Synopsis: Text-to-SQL <llamaindex-synopsis>`
-- {ref}`Tutorial: Text-to-SQL using Azure <llamaindex-usage-azure>`
-- [Examples: ML]
-
-::::
-
-:::::
 
 :::{rubric} Use cases
 :::
@@ -232,6 +195,39 @@ Learn more about how to combine ML features with other major features of CrateDB
 | Time-series support | Perfect for sensor-based training data      | {ref}`timeseries`                      |
 
 
+## Further reading
+
+:::::{grid} 1 3 3 3
+:margin: 4 4 0 0
+:padding: 0
+:gutter: 2
+
+::::{grid-item-card} {material-outlined}`article;1.5em` Documentation
+:columns: 3
+- {ref}`vector-search`
+- {ref}`hybrid-search`
+- {ref}`Machine Learning <ml>`
+::::
+
+::::{grid-item-card} {material-outlined}`integration_instructions;1.5em` Integrations
+:columns: 3
+- {ref}`langchain`
+- {ref}`llamaindex`
+- {ref}`mindsdb`
+- {ref}`mlflow`
+- {ref}`pycaret`
+::::
+
+::::{grid-item-card} {material-outlined}`read_more;1.5em` Read more
+:columns: 6
+- [Blog: Vector support and KNN search]
+- {ref}`Synopsis: Text-to-SQL <llamaindex-synopsis>`
+- {ref}`Tutorial: Text-to-SQL using Azure <llamaindex-usage-azure>`
+- [Examples: ML]
+::::
+
+:::::
+
 
 [Blog: Vector support and KNN search]: https://cratedb.com/blog/unlocking-the-power-of-vector-support-and-knn-search-in-cratedb
 [Examples: ML]: https://github.com/crate/cratedb-examples/tree/main/topic/machine-learning
