Updated docs.

Author: igorborgest

README.md

@@ -2,6 +2,8 @@
 
 *Pandas on AWS*
 
+Easy integration with Athena, Glue, Redshift, Timestream, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).
+
 ![AWS Data Wrangler](docs/source/_static/logo2.png?raw=true "AWS Data Wrangler")
 
 > An [AWS Professional Service](https://aws.amazon.com/professional-services/) open source initiative | [email protected]
@@ -30,7 +32,7 @@ Powered By [<img src="https://arrow.apache.org/img/arrow.png" width="200">](http
 - [Community Resources](#community-resources)
 - [Logging](#logging)
 - [Who uses AWS Data Wrangler?](#who-uses-aws-data-wrangler)
-- [Amazon SageMaker Data Wrangler?](#amazon-sageMaker-data-wrangler)
+- [What is Amazon SageMaker Data Wrangler?](#amazon-sageMaker-data-wrangler)
 
 ## Quick Start
 

docs/source/install.rst

@@ -170,14 +170,13 @@ After installing these header files you can either just install ``pyodbc`` or
     >>> pip install pyodbc
     >>> pip install awswrangler[sqlserver]
 
-Finally you also the need correct ODBC Driver for SQL Server. You can have a look at the
+Finally you also need the correct ODBC Driver for SQL Server. You can have a look at the
 `documentation from Microsoft <https://docs.microsoft.com/sql/connect/odbc/
 microsoft-odbc-driver-for-sql-server?view=sql-server-ver15>`_
 to see how they can be installed in your environment.
 
 If you want to connect to Microsoft SQL Server from AWS Lambda, you can build a separate Layer including the
-needed OBDC drivers and `pyobdc` by following
-`these instructions <https://gist.github.com/diriver63/b72a954fa0da4851d89e5086aa13c6e8>`_.
+needed OBDC drivers and `pyobdc`.
 
 If you maintain your own environment, you need to take care of the above steps.
 Because of this limitation usage in combination with Glue jobs is limited and you need to rely on the

docs/source/what.rst

@@ -1,7 +1,9 @@
 What is AWS Data Wrangler?
 ==========================
 
-An `AWS Professional Service <https://aws.amazon.com/professional-services>`_ `open source <https://github.com/awslabs/aws-data-wrangler>`_ python initiative that extends the power of `Pandas <https://github.com/pandas-dev/pandas>`_ library to AWS connecting **DataFrames** and AWS data related services (**Amazon Redshift**, **AWS Glue**, **Amazon Athena**, **Amazon Timestream**, **Amazon EMR**, **Amazon QuickSight**, etc).
+An `AWS Professional Service <https://aws.amazon.com/professional-services>`_ `open source <https://github.com/awslabs/aws-data-wrangler>`_ python initiative that extends the power of `Pandas <https://github.com/pandas-dev/pandas>`_ library to AWS connecting **DataFrames** and AWS data related services.
+
+Easy integration with Athena, Glue, Redshift, Timestream, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).
 
 Built on top of other open-source projects like `Pandas <https://github.com/pandas-dev/pandas>`_, `Apache Arrow <https://github.com/apache/arrow>`_ and `Boto3 <https://github.com/boto/boto3>`_, it offers abstracted functions to execute usual ETL tasks like load/unload data from **Data Lakes**, **Data Warehouses** and **Databases**.