Merge branch 'develop' into docs/add_data_catalog_section

README.md

@@ -100,7 +100,7 @@ YData-profiling can be used to deliver a variety of different use-case. The docu
 | [Handling sensitive data](https://ydata-profiling.ydata.ai/docs/master/pages/use_cases/sensitive_data.html )                       | Generating reports which are mindful about sensitive data in the input dataset              |
 | [Dataset metadata and data dictionaries](https://ydata-profiling.ydata.ai/docs/master/pages/use_cases/metadata.html)               | Complementing the report with dataset details and column-specific data dictionaries         |
 | [Customizing the report's appearance](https://ydata-profiling.ydata.ai/docs/master/pages/use_cases/custom_report_appearance.html ) | Changing the appearance of the report's page and of the contained visualizations            |
-
+| [Profiling Databases](https://ydata-profiling.ydata.ai/docs/master/pages/use_cases/profiling_databases.html) | For a seamless profiling experience in your organization's databases, check [Fabric Data Catalog](https://ydata.ai/products/data_catalog), which allows to consume data from different types of storages such as RDBMs (Azure SQL, PostGreSQL, Oracle, etc.) and object storages (Google Cloud Storage, AWS S3, Snowflake, etc.), among others. |
 ### Using inside Jupyter Notebooks
 
 There are two interfaces to consume the report inside a Jupyter notebook: through widgets and through an embedded HTML report.

docsrc/source/index.rst

@@ -19,6 +19,7 @@
    pages/use_cases/big_data
    pages/use_cases/sensitive_data
    pages/use_cases/comparing_datasets
+   pages/use_cases/profiling_databases
    pages/use_cases/metadata
    pages/use_cases/custom_report_appearance
 

docsrc/source/pages/use_cases/profiling_databases.rst

@@ -0,0 +1,20 @@
+==================
+Profiling Databases
+==================
+
+``ydata-profiling`` provides overall metrics and statistics for your datasets. However, data is often not stored in a single table but rather across multiple tables in a database.
+
+Databases consist of schemas, each composed of several tables. Being able to profile the relationships between tables, such as primary keys and foreign keys relationships,
+while being able to combine it with detailed statistics and metrics per table is crucial to ensure data integrity , effective data management and avoid anomalies.
+
+`YData Fabric Data Catalog <https://ydata.ai/products/data_catalog>`_ delivers detailed databases data quality insights and profiling metrics combined with built-in connectors
+for easy integration with existing data architectures. With a Fabric Data Catalog, you can:
+
+* Connect to existing data architecture through built-in data connectors
+* Manage data connections and dataset ownership
+* Organize data on a project basis
+* Analyze the schema and profile referential integrity
+* View detailed statistics and plots per table
+* Experience an interactive interface that supports tables with hundreds of columns through distributed computing
+
+`Learn more about the benefits of adopting Data Catalogs. <https://ydata.ai/resources/unlocking-the-power-of-a-data-catalog-for-your-business>`_.