Integrate: Migrate pages from crate-clients-tools, to be retired soon

docs/_include/links.md

@@ -19,6 +19,7 @@
 [DynamoDB CDC Relay]: https://cratedb-toolkit.readthedocs.io/io/dynamodb/cdc.html
 [DynamoDB CDC Relay with AWS Lambda]: https://cratedb-toolkit.readthedocs.io/io/dynamodb/cdc-lambda.html
 [DynamoDB Table Loader]: https://cratedb-toolkit.readthedocs.io/io/dynamodb/loader.html
+[Executable stack with Apache Kafka, Apache Flink, and CrateDB]: https://github.com/crate/cratedb-examples/tree/main/framework/flink/kafka-jdbcsink-java
 [Geospatial Data Model]: https://cratedb.com/data-model/geospatial
 [Geospatial Database]: https://cratedb.com/geospatial-spatial-database
 [HNSW]: https://en.wikipedia.org/wiki/Hierarchical_navigable_small_world

docs/conf.py

@@ -53,6 +53,8 @@
     r"https://dzone.com/.*",
     # 504 Client Error: Gateway Timeout for url
     r"https://web.archive.org/.*",
+    # 403 Client Error: Forbidden for url
+    r"https://www.tableau.com/",
 ]
 
 linkcheck_anchors_ignore_for_url += [

docs/connect/df/index.md

@@ -1,40 +1,16 @@
 (df)=
 (dataframe)=
 (dataframes)=
+(dataframe-examples)=
 # CrateDB and DataFrame libraries
 
-Data frame libraries and frameworks which can
-be used together with CrateDB.
-
-
-:::::{grid} 1 2 2 2
-:margin: 4 4 0 0
-:padding: 0
-:gutter: 2
-
-::::{grid-item-card} {material-outlined}`lightbulb;2em` Tutorials
-:link: guide:dataframes
-:link-type: ref
-Learn how to use CrateDB together with popular open-source data frame
-libraries, on behalf of hands-on tutorials and code examples.
-+++
-{tag-info}`Dask` {tag-info}`pandas` {tag-info}`Polars`
-::::
-
-::::{grid-item-card} {material-outlined}`read_more;2em` SQLAlchemy
-CrateDB's SQLAlchemy dialect implementation provides fundamental infrastructure
-to integrations with Dask, pandas, and Polars.
-+++
-[ORM Guides](inv:guide#orm) •
-{ref}`ORM Catalog <orm>`
-::::
-
-:::::
-
+How to use CrateDB together with popular open-source DataFrame libraries.
 
 (dask)=
 ## Dask
 
+:::{rubric} About
+:::
 [Dask] is a parallel computing library for analytics with task scheduling.
 It is built on top of the Python programming language, making it easy to scale
 the Python libraries that you know and love, like NumPy, pandas, and scikit-learn.
@@ -56,10 +32,20 @@ the Python libraries that you know and love, like NumPy, pandas, and scikit-lear
 :style: "clear: both"
 ```
 
+:::{rubric} Learn
+:::
+- [Guide to efficient data ingestion to CrateDB with pandas and Dask]
+- [Efficient batch/bulk INSERT operations with pandas, Dask, and SQLAlchemy]
+- [Import weather data using Dask]
+- [Dask code examples]
+
 
 (pandas)=
 ## pandas
 
+:::{rubric} About
+:::
+
 ```{div}
 :style: "float: right"
 [![](https://pandas.pydata.org/static/img/pandas.svg){w=180px}](https://pandas.pydata.org/)
@@ -92,10 +78,21 @@ and operations for manipulating numerical tables and time series.
 :style: "clear: both"
 ```
 
+:::{rubric} Learn
+:::
+- [Guide to efficient data ingestion to CrateDB with pandas]
+- [Importing Parquet files into CrateDB using Apache Arrow and SQLAlchemy]
+- [pandas code examples]
+- [From data storage to data analysis: Tutorial on CrateDB and pandas]
+
+
 
 (polars)=
 ## Polars
 
+:::{rubric} About
+:::
+
 ```{div}
 :style: "float: right; margin-left: 0.5em"
 [![](https://github.com/pola-rs/polars-static/raw/master/logos/polars-logo-dark.svg){w=180px}](https://pola.rs/)
@@ -147,13 +144,9 @@ This allows you to easily integrate Polars into your existing data stack.
 :style: "clear: both"
 ```
 
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-learn
-```
+:::{rubric} Learn
+:::
+- [Polars code examples]
 
 
 [Apache Arrow]: https://arrow.apache.org/
@@ -162,3 +155,13 @@ learn
 [Dask Futures]: https://docs.dask.org/en/latest/futures.html
 [pandas]: https://pandas.pydata.org/
 [Polars]: https://pola.rs/
+
+[Dask code examples]: https://github.com/crate/cratedb-examples/tree/main/by-dataframe/dask
+[Efficient batch/bulk INSERT operations with pandas, Dask, and SQLAlchemy]: https://cratedb.com/docs/python/en/latest/by-example/sqlalchemy/dataframe.html
+[From data storage to data analysis: Tutorial on CrateDB and pandas]: https://community.cratedb.com/t/from-data-storage-to-data-analysis-tutorial-on-cratedb-and-pandas/1440
+[Guide to efficient data ingestion to CrateDB with pandas]: https://community.cratedb.com/t/guide-to-efficient-data-ingestion-to-cratedb-with-pandas/1541
+[Guide to efficient data ingestion to CrateDB with pandas and Dask]: https://community.cratedb.com/t/guide-to-efficient-data-ingestion-to-cratedb-with-pandas-and-dask/1482
+[Import weather data using Dask]: https://github.com/crate/cratedb-examples/blob/main/topic/timeseries/dask-weather-data-import.ipynb
+[Importing Parquet files into CrateDB using Apache Arrow and SQLAlchemy]: https://community.cratedb.com/t/importing-parquet-files-into-cratedb-using-apache-arrow-and-sqlalchemy/1161
+[pandas code examples]: https://github.com/crate/cratedb-examples/tree/main/by-dataframe/pandas
+[Polars code examples]: https://github.com/crate/cratedb-examples/tree/main/by-dataframe/polars

docs/connect/index.md

@@ -151,9 +151,9 @@ Python
 
 configure
 Drivers <drivers>
-CLI <cli>
-DataFrame <df/index>
-ORM <orm>
+CLI programs <cli>
+DataFrame libraries <df/index>
+ORM libraries <orm>
 ```
 
 ```{toctree}

docs/domain/index.md

@@ -23,7 +23,7 @@ telemetry/index
 analytics/index
 industrial/index
 timeseries/index
-ml/index
+Machine Learning <ml/index>
 ```
 
 

docs/domain/ml/index.md

@@ -1,13 +1,16 @@
 (ml)=
+(ml-tools)=
 (machine-learning)=
-
-# Machine Learning
+# Machine Learning with CrateDB
 
 :::{include} /_include/links.md
 :::
 :::{include} /_include/styles.html
 :::
 
+Machine learning applications and frameworks
+which can be used together with CrateDB.
+
 Integrate CrateDB with machine learning frameworks and
 tools, for MLOps and vector database operations.
 
@@ -60,6 +63,27 @@ generation (RAG), and other applications.
 (mlflow)=
 ### MLflow
 
+:::{rubric} About
+:::
+```{div}
+:style: "float: right; margin-left: 1em"
+[![](https://github.com/crate/crate-clients-tools/assets/453543/d1d4f4ac-1b44-46b8-ba6f-4a82607c57d3){w=180px}](https://mlflow.org/)
+```
+
+[MLflow] is an open source platform to manage the whole ML lifecycle, including
+experimentation, reproducibility, deployment, and a central model registry.
+
+The [MLflow adapter for CrateDB], available through the [mlflow-cratedb] package,
+provides support to use CrateDB as a storage database for the [MLflow Tracking]
+subsystem, which is about recording and querying experiments, across code, data,
+config, and results.
+
+```{div}
+:style: "clear: both"
+```
+
+:::{rubric} Learn
+:::
 Tutorials and Notebooks about using [MLflow] together with CrateDB.
 
 ::::{info-card}
@@ -109,6 +133,28 @@ prediction using NumPy, Salesforce Merlion, and Matplotlib.
 (pycaret)=
 ### PyCaret
 
+:::{rubric} About
+:::
+```{div}
+:style: "float: right; margin-left: 1em"
+[![](https://github.com/crate/crate-clients-tools/assets/453543/b17a59e2-6801-4f53-892f-ff472491095f){w=180px}](https://pycaret.org/)
+```
+
+[PyCaret] is an open-source, low-code machine learning library for Python that
+automates machine learning workflows.
+
+It is a high-level interface and AutoML wrapper on top of your loved machine learning
+libraries like scikit-learn, xgboost, ray, lightgbm, and many more. PyCaret provides a
+universal interface to utilize these libraries without needing to know the details
+of the underlying model architectures and parameters.
+
+```{div}
+:style: "clear: both"
+```
+
+:::{rubric} Learn
+:::
+
 Tutorials and Notebooks about using [PyCaret] together with CrateDB.
 
 ::::{info-card}
@@ -154,9 +200,50 @@ How to train time series forecasting models using PyCaret and CrateDB.
 ::::
 
 
+(iris-r)=
+### R
+
+Use R with CrateDB.
+
+:::::{info-card}
+::::{grid-item}
+:columns: 9
+**Statistical analysis and visualization on huge datasets**
+
+Details about how to create a machine learning pipeline
+using R and CrateDB.
+
+:::{toctree}
+:maxdepth: 1
+
+r
+:::
+
+::::
+::::{grid-item}
+:columns: 3
+{tags-primary}`Fundamentals`
+::::
+:::::
+
+
 (scikit-learn)=
 ### scikit-learn
 
+:::{rubric} About
+:::
+```{div}
+:style: "float: right; margin-left: 1em"
+[![](https://upload.wikimedia.org/wikipedia/commons/thumb/0/05/Scikit_learn_logo_small.svg/240px-Scikit_learn_logo_small.svg.png){w=180px}](https://scikit-learn.org/)
+
+[![](https://pandas.pydata.org/static/img/pandas.svg){w=180px}](https://pandas.pydata.org/)
+
+[![](https://jupyter.org/assets/logos/rectanglelogo-greytext-orangebody-greymoons.svg){w=180px}](https://jupyter.org/)
+```
+
+:::{rubric} Learn
+:::
+
 Use [scikit-learn] with CrateDB.
 
 ::::{info-card}
@@ -283,7 +370,10 @@ tensorflow
 [Machine Learning and CrateDB: Getting Started With Jupyter]: https://cratedb.com/blog/machine-learning-cratedb-jupyter
 [Machine Learning and CrateDB: Experiment Design & Linear Regression]: https://cratedb.com/blog/machine-learning-and-cratedb-part-three-experiment-design-and-linear-regression
 [MLflow]: https://mlflow.org/
+[MLflow adapter for CrateDB]: https://github.com/crate/mlflow-cratedb
 [MLflow and CrateDB]: https://github.com/crate/cratedb-examples/tree/main/topic/machine-learning/mlflow
+[mlflow-cratedb]: https://pypi.org/project/mlflow-cratedb/
+[MLflow Tracking]: https://mlflow.org/docs/latest/tracking.html
 [MLOps]: https://en.wikipedia.org/wiki/MLOps
 [pandas]: https://pandas.pydata.org/
 [PyCaret]: https://www.pycaret.org