Theme: Use modernized crate-docs-theme

It includes two bugfix iterations already, so this is a good chance to
test it in production.

Author: amotl

docs/_include/card/timeseries-datashader.md

@@ -1,7 +1,8 @@
 ::::{info-card}
 
-:::{grid-item} **Display millions of data points using hvPlot, Datashader, and CrateDB**
+:::{grid-item}
 :columns: auto 9 9 9
+**Display millions of data points using hvPlot, Datashader, and CrateDB**
 
 [HoloViews] and [Datashader] frameworks enable channeling millions of data
 points from your backend systems to the browser's glass.

docs/_include/card/timeseries-explore.md

@@ -1,7 +1,8 @@
 ::::{info-card}
 
-:::{grid-item} **CrateDB for Time Series Modeling, Exploration, and Visualization**
+:::{grid-item}
 :columns: auto 9 9 9
+**CrateDB for Time Series Modeling, Exploration, and Visualization**
 
 Access time series data from CrateDB via SQL, load it into pandas DataFrames,
 and visualize it using Plotly.

docs/domain/analytics/index.md

@@ -82,7 +82,7 @@ to deal with so many data records and keep them all available for querying in
 real time.
 :::
 
-:::{grid-item}  
+:::{grid-item}
 :columns: 4
 
 <iframe width="240" src="https://www.youtube-nocookie.com/embed/4BPApD0Piyc?si=J0w5yG56Ld4fIXfm" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

docs/domain/industrial/index.md

@@ -91,7 +91,7 @@ With high scalability and easy integration, CrateDB is a strategic database
 component of ABB Ability™ Genix across industries.
 :::
 
-:::{grid-item}  
+:::{grid-item}
 :columns: 4
 
 <iframe width="240" src="https://www.youtube-nocookie.com/embed/45fZYJLh2Qg?si=J0w5yG56Ld4fIXfm" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
@@ -147,7 +147,7 @@ made Rauch choose it over other databases, such as PostgreSQL compatibility,
 the support for unstructured data, and its excellent customer support.
 :::
 
-:::{grid-item} &nbsp;
+:::{grid-item}
 :columns: 4
 
 <iframe width="240" src="https://www.youtube-nocookie.com/embed/gJPmJ0uXeVs?si=J0w5yG56Ld4fIXfm" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
@@ -204,7 +204,7 @@ help the mining businesses save resources, workforce, and losses, due to
 decreased downtime.
 :::
 
-:::{grid-item} &nbsp;
+:::{grid-item}
 :columns: 4
 
 <iframe width="240" src="https://www.youtube-nocookie.com/embed/eRqn7GhFO-s?si=J0w5yG56Ld4fIXfm" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
@@ -260,7 +260,7 @@ migration path, it was easy to start with CrateDB, and now it is at the
 heart of everything they are doing, and gives them peace of mind.
 :::
 
-:::{grid-item} &nbsp;
+:::{grid-item}
 :columns: 4
 
 <iframe width="240" src="https://www.youtube-nocookie.com/embed/X2o0-W8-mCM?si=J0w5yG56Ld4fIXfm" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
@@ -298,7 +298,7 @@ its ability to aggregate different data formats and the ability to query this
 information without further ado.
 :::
 
-:::{grid-item} &nbsp;
+:::{grid-item}
 :columns: 4
 
 <iframe width="240" src="https://www.youtube-nocookie.com/embed/6dgjVQJtSKI?si=J0w5yG56Ld4fIXfm" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
@@ -342,7 +342,7 @@ high variety, unstructured features, and at different data frequencies.
 
 :::
 
-:::{grid-item} &nbsp;
+:::{grid-item}
 :columns: 4
 
 <iframe width="240" src="https://www.youtube-nocookie.com/embed/ugQvihToY0k?si=J0w5yG56Ld4fIXfm" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

docs/domain/ml/index.md

@@ -216,8 +216,9 @@ or RAG. RAG is a technique for augmenting LLM knowledge with additional data.
 
 ::::{info-card}
 
-:::{grid-item} **How to Use Private Data in Generative AI**
+:::{grid-item}
 :columns: auto auto 8 8
+**How to Use Private Data in Generative AI**
 
 In this video recorded at FOSDEM 2024, we explain how to leverage private data
 in generative AI on behalf of an end-to-end Retrieval Augmented Generation (RAG)

docs/domain/telemetry/index.md

@@ -74,7 +74,7 @@ to use CrateDB as remote storage.
 [Prometheus with CrateDB: Long Term Metrics Storage]
 :::
 
-:::{grid-item}  
+:::{grid-item}
 :columns: 4
 
 <iframe width="240" src="https://www.youtube-nocookie.com/embed/EfIlRXVyfZM?si=J0w5yG56Ld4fIXfm" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

docs/domain/timeseries/advanced.md

@@ -34,8 +34,9 @@ detection and/or [forecasting] methods.
 
 ::::{info-card}
 
-:::{grid-item} **Use MLflow for time series anomaly detection and time series forecasting**
+:::{grid-item}
 :columns: auto 9 9 9
+**Use MLflow for time series anomaly detection and time series forecasting**
 
 Guidelines and runnable code to get started with [MLflow] and CrateDB, exercising
 time series anomaly detection and time series forecasting / prediction using
@@ -58,8 +59,9 @@ NumPy, Merlion, and Matplotlib.
 
 ::::{info-card}
 
-:::{grid-item} **Use PyCaret to train time series forecasting models**
+:::{grid-item}
 :columns: auto 9 9 9
+**Use PyCaret to train time series forecasting models**
 
 This notebook explores the [PyCaret] framework and shows how to use it
 to train various time series forecasting models.
@@ -107,8 +109,9 @@ effectively.
 
 ::::{info-card}
 
-:::{grid-item} **Analyze trend, seasonality, and fluctuations with PyCaret and CrateDB**
+:::{grid-item}
 :columns: auto 9 9 9
+**Analyze trend, seasonality, and fluctuations with PyCaret and CrateDB**
 
 Learn how to extract data from CrateDB for analysis in PyCaret, how to
 further preprocess it and how to use PyCaret to plot time series
@@ -148,8 +151,9 @@ engineering and model building.
 
 ::::{info-card}
 
-:::{grid-item} **Exploratory data analysis (EDA) with PyCaret and CrateDB**
+:::{grid-item}
 :columns: auto 9 9 9
+**Exploratory data analysis (EDA) with PyCaret and CrateDB**
 
 Learn how to access time series data from CrateDB using SQL, and how to apply
 exploratory data analysis (EDA) with PyCaret.
@@ -189,8 +193,9 @@ operations.
 
 ::::{info-card}
 
-:::{grid-item} **Analyzing Device Readings with Metadata Integration**
+:::{grid-item}
 :columns: auto 9 9 9
+**Analyzing Device Readings with Metadata Integration**
 
 This tutorial illustrates how to augment time series data with metadata, in
 order to enable more comprehensive analysis. It uses a time series dataset that
@@ -220,8 +225,9 @@ CrateDB offers enhanced features for analysing time series data.
 
 ::::{info-card}
 
-:::{grid-item} **Analyzing Weather Data**
+:::{grid-item}
 :columns: 9
+**Analyzing Weather Data**
 
 Run aggregations with gap filling / interpolation, using common
 table expressions (CTEs) and LAG / LEAD window functions.

docs/domain/timeseries/longterm.md

@@ -13,8 +13,9 @@ and insightful analysis, even considering historic data records.
 
 ::::{info-card}
 
-:::{grid-item} **Optimizing storage for historic time series data**
+:::{grid-item}
 :columns: auto 9 9 9
+**Optimizing storage for historic time series data**
 
 This tutorial illustrates how to reduce table storage size by 80%,
 by using arrays for time-based bucketing, a historical table having
@@ -36,8 +37,9 @@ a dedicated layout, and querying using the UNNEST table function.
 
 ::::{info-card}
 
-:::{grid-item} **Notebook: How to Build Time Series Applications with CrateDB**
+:::{grid-item}
 :columns: auto 9 9 9
+**Notebook: How to Build Time Series Applications with CrateDB**
 
 This notebook illustrates how to import and work with time series data in CrateDB.
 It uses Dask to import data into CrateDB.
@@ -63,8 +65,9 @@ Dask is a framework to parallelize operations on pandas data frames.
 
 ::::{info-card}
 
-:::{grid-item} **CrateDB as metrics and log data store for the long term**
+:::{grid-item}
 :columns: auto 9 9 9
+**CrateDB as metrics and log data store for the long term**
 
 Store and analyze high volumes of system monitoring information.
 Read more about using CrateDB as [](#metrics-store).
@@ -82,8 +85,9 @@ Read more about using CrateDB as [](#metrics-store).
 
 ::::{info-card}
 
-:::{grid-item} **CrateDB provides real-time analytics on raw data stored for the long term**
+:::{grid-item}
 :columns: auto 9 9 9
+**CrateDB provides real-time analytics on raw data stored for the long term**
 
 Keep massive amounts of data ready in the hot zone for analytics purposes.
 Read more about using CrateDB for [](#analytics).
@@ -103,8 +107,9 @@ Read more about using CrateDB for [](#analytics).
 
 ::::{info-card}
 
-:::{grid-item} **Storing and analyzing massive amounts of synoptic weather data**
+:::{grid-item}
 :columns: auto 8 8 8
+**Storing and analyzing massive amounts of synoptic weather data**
 
 Wetterdienst uses CrateDB for mass storage of weather data, allowing you to
 query it efficiently. It provides access to data at more than ten canonical

docs/domain/timeseries/video.md

@@ -8,8 +8,9 @@ Video tutorials about time series with CrateDB.
 
 ::::{info-card}
 
-:::{grid-item} **A collection of videos about how CrateDB deals with time-series data**
+:::{grid-item}
 :columns: 9
+**A collection of videos about how CrateDB deals with time-series data**
 
 <iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/videoseries?si=C5ayK8bqkhRYovjc&amp;list=PLDZqzXOGoWUKTZwR7zOY8s1sTvZOAa7cy" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
 
@@ -22,7 +23,7 @@ insights from large-scale and high-volume time-series datasets.
 Learn more about CrateDB and [](#timeseries).
 :::
 
-:::{grid-item} &nbsp;
+:::{grid-item}
 :columns: 3
 
 {tags-secondary}`Introduction` \
@@ -38,8 +39,9 @@ Learn more about CrateDB and [](#timeseries).
 
 ::::{info-card}
 
-:::{grid-item} **The basics of `COPY FROM` and `COPY TO`**
+:::{grid-item}
 :columns: 9
+**The basics of `COPY FROM` and `COPY TO`**
 
 <iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/xDypaX37XZQ?si=J0w5yG56Ld4fIXfm" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
 
@@ -55,7 +57,7 @@ For more information about how to import and export
 data from/into CrateDB, please refer to [](#import-export).
 :::
 
-:::{grid-item} &nbsp;
+:::{grid-item}
 :columns: 3
 
 {tags-secondary}`Introduction` \
@@ -72,8 +74,9 @@ data from/into CrateDB, please refer to [](#import-export).
 
 ::::{info-card}
 
-:::{grid-item} **From raw data to fast analysis in only three steps**
+:::{grid-item}
 :columns: 9
+**From raw data to fast analysis in only three steps**
 
 <iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/7biXPnG7dY4?si=J0w5yG56Ld4fIXfm" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
 
@@ -91,7 +94,7 @@ Our speakers will also show you how to find the right sharding and partitioning
 strategy for your time series data in CrateDB.
 :::
 
-:::{grid-item} &nbsp;
+:::{grid-item}
 :columns: 3
 
 {tags-secondary}`Extensive` \
@@ -110,16 +113,17 @@ strategy for your time series data in CrateDB.
 
 ::::{info-card}
 
-:::{grid-item} **Real-time analytics on raw tracking data**
+:::{grid-item}
 :columns: 9
+**Real-time analytics on raw tracking data**
 
 Learn how Bitmovin leverages CrateDB to support real-time analytics on
 top of tracking data from their video streaming broadcasting system.
 
 - [](#bitmovin)
 :::
 
-:::{grid-item} &nbsp;
+:::{grid-item}
 :columns: 3
 
 {tags-secondary}`Extensive` \
@@ -152,7 +156,7 @@ world.
 - [](#tgw)
 :::
 
-:::{grid-item} &nbsp;
+:::{grid-item}
 :columns: 3
 
 {tags-secondary}`Extensive` \