Tutorials: Re-add references to tutorials refactored elsewhere

Author: amotl

docs/index.md

@@ -94,6 +94,12 @@ A command-line based terminal program to operate your managed clusters.
 :::
 
 
+:::{grid-item-card} {octicon}`mortar-board` Tutorials
+:link: usage
+:link-type: ref
+Learn how to use key features of CrateDB SQL.
+:::
+
 :::{grid-item-card} {octicon}`container` CrateDB Cloud on Kubernetes
 :link: cloud-on-kubernetes
 :link-type: ref
@@ -153,6 +159,7 @@ for improvements], and [source code contributions], are always welcome. {fab}`gi
 :hidden:
 
 Quick Start <tutorials/quick-start>
+Usage <tutorials/usage>
 Services <reference/services>
 Billing <reference/billing>
 Manage <howtos/index>

docs/tutorials/usage.md

@@ -0,0 +1,84 @@
+(usage)=
+
+# Using SQL on CrateDB Cloud
+
+Learn how to use key features of CrateDB SQL using fundamental tutorials,
+or explore {ref}`guide:features`.
+
+<style>
+/* Cards with Links */
+.sd-hide-link-text {
+  height: 0;
+}
+</style>
+
+
+:::::{grid} auto 2 2 2
+:margin: 4 4 0 0
+:padding: 0
+:gutter: 2
+
+::::{grid-item-card} {material-outlined}`data_object;2em` Document Store: The OBJECT Data Type
+:link: guide:objects-basics
+:link-type: ref
+:class-footer: text-smaller
+
+CrateDB’s `OBJECT` data type allows to store and analyze complex and nested data
+efficiently. It can optionally be strict or dynamic, thus schemaless.
+
+The tutorial explores analyzing marketing data, therefore it also outlines another
+feature of CrateDB, supporting destructuring URLs by using generated columns.
++++
+CrateDB's document store is based on Lucene indexes, exactly how Elasticsearch
+is doing it.
+::::
+
+::::{grid-item-card} {material-outlined}`topic;2em` Time Series: Device Readings with Metadata
+:link: guide:timeseries-objects
+:link-type: ref
+:class-footer: text-smaller
+
+CrateDB supports effective time series analysis with enhanced features
+for fast aggregations.
+
+- Rich data types for storing structured nested data (OBJECT) alongside
+  time series data.
+- A rich set of built-in functions for aggregations.
+- Relational JOIN operations.
+- Common table expressions (CTEs).
++++
+Combine time series data with document data: CrateDB is all you need.
+::::
+
+::::{grid-item-card} {material-outlined}`search;2em` Full-Text: Explore the Netflix Catalog
+:link: guide:search-basics
+:link-type: ref
+:class-footer: text-smaller
+CrateDB's `TEXT INDEX USING FULLTEXT` SQL DDL clause sets up a full-text index
+on a column. The `MATCH` SQL predicate is used for querying it.
+
+The tutorial explores the Netflix Catalog, exercising FTS features on relevant data.
++++
+CrateDB's full-text search is based on Lucene's inverted index and BM25 scoring.
+::::
+
+::::{grid-item-card} {material-outlined}`lightbulb;2em` Time Series: Advanced SQL
+:link: guide:timeseries-analysis-weather
+:link-type: ref
+:class-footer: text-smaller
+CrateDB provides enhanced features for querying time series data.
+
+Run aggregations with gap filling / interpolation, using common
+table expressions (CTEs) and LAG / LEAD window functions.
+
+Find maximum values using the MAX_BY aggregate function, returning
+the value from one column based on the maximum or minimum value of another
+column within a group.
+
+The tutorial analyzes data from synoptic weather observations.
++++
+Advanced queries on time series data: CrateDB is all you need.
+::::
+
+
+:::::