From e33602ff4800719b8e3bc85981e8d7977c315376 Mon Sep 17 00:00:00 2001
From: Miriam Seoane Santos <68821478+miriamspsantos@users.noreply.github.com>
Date: Fri, 21 Jul 2023 10:57:34 +0100
Subject: [PATCH] docs: update overview

---
 docsrc/source/pages/getting_started/overview.rst | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/docsrc/source/pages/getting_started/overview.rst b/docsrc/source/pages/getting_started/overview.rst
index 26baa6070..63454326c 100644
--- a/docsrc/source/pages/getting_started/overview.rst
+++ b/docsrc/source/pages/getting_started/overview.rst
@@ -35,11 +35,6 @@ Overview
 
 The package outputs a simple and digested analysis of a dataset, including **time-series** and **text**.
 
-.. NOTE::
-   **Looking for a scalable solution that can fully integrate with your database systems?**
-   Leverage `YData Fabric Data Catalog <https://ydata.ai/products/data_catalog>`_ to connect to different databases and storages (Oracle, snowflake, PostGreSQL, GCS, S3, etc.), 
-   and leverage an interactive and guided profiling experience in Fabric. Check out the `Community Version <https://ydata.ai/ydata-fabric-free-trial>`_.
-
 Key features
 ------------
 - **Type inference**: automatic detection of columns' data types (*Categorical*, *Numerical*, *Date*, etc.)
@@ -58,4 +53,9 @@ The report contains three additional sections:
 * **Alerts**: a comprehensive and automatic list of potential data quality issues (high correlation, imbalance, skewness, uniformity, zeros, missing values, constant values, between others) 
 * **Reproduction**: technical details about the analysis (time, version and configuration)
 
-The package can be used via code but also directly as a CLI utility. The generated interactive report can be consumed and shared as regular HTML or embedded in an interactive way inside Jupyter Notebooks.
\ No newline at end of file
+The package can be used via code but also directly as a CLI utility. The generated interactive report can be consumed and shared as regular HTML or embedded in an interactive way inside Jupyter Notebooks.
+
+.. NOTE::
+   **Looking for a scalable solution that can fully integrate with your database systems?**
+   Leverage `YData Fabric Data Catalog <https://ydata.ai/products/data_catalog>`_ to connect to different databases and storages (Oracle, snowflake, PostGreSQL, GCS, S3, etc.), 
+   and leverage an interactive and guided profiling experience in Fabric. Check out the `Community Version <https://ydata.ai/ydata-fabric-free-trial>`_.