Bumping version to 2.0.0.

Author: igorborgest

README.md

@@ -6,7 +6,7 @@
 
 > An [AWS Professional Service](https://aws.amazon.com/professional-services/) open source initiative | [email protected]
 
-[![Release](https://img.shields.io/badge/release-1.10.1-brightgreen.svg)](https://pypi.org/project/awswrangler/)
+[![Release](https://img.shields.io/badge/release-2.0.0-brightgreen.svg)](https://pypi.org/project/awswrangler/)
 [![Python Version](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8-brightgreen.svg)](https://anaconda.org/conda-forge/awswrangler)
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
@@ -56,17 +56,10 @@ df = wr.s3.read_parquet("s3://bucket/dataset/", dataset=True)
 # Retrieving the data from Amazon Athena
 df = wr.athena.read_sql_query("SELECT * FROM my_table", database="my_db")
 
-# Get Redshift connection (SQLAlchemy) from Glue and retrieving data from Redshift Spectrum
-engine = wr.catalog.get_engine("my-redshift-connection")
-df = wr.db.read_sql_query("SELECT * FROM external_schema.my_table", con=engine)
-
-# Get MySQL connection (SQLAlchemy) from Glue Catalog and LOAD the data into MySQL
-engine = wr.catalog.get_engine("my-mysql-connection")
-wr.db.to_sql(df, engine, schema="test", name="my_table")
-
-# Get PostgreSQL connection (SQLAlchemy) from Glue Catalog and LOAD the data into PostgreSQL
-engine = wr.catalog.get_engine("my-postgresql-connection")
-wr.db.to_sql(df, engine, schema="test", name="my_table")
+# Get a Redshift connection from Glue Catalog and retrieving data from Redshift Spectrum
+con = wr.redshift.connect("my-glue-connection")
+df = wr.redshift.read_sql_query("SELECT * FROM external_schema.my_table", con=con)
+con.close()
 ```
 
 ## [Read The Docs](https://aws-data-wrangler.readthedocs.io/)
@@ -112,7 +105,10 @@ wr.db.to_sql(df, engine, schema="test", name="my_table")
   - [Amazon S3](https://aws-data-wrangler.readthedocs.io/en/stable/api.html#amazon-s3)
   - [AWS Glue Catalog](https://aws-data-wrangler.readthedocs.io/en/stable/api.html#aws-glue-catalog)
   - [Amazon Athena](https://aws-data-wrangler.readthedocs.io/en/stable/api.html#amazon-athena)
-  - [Databases (Amazon Redshift, PostgreSQL, MySQL)](https://aws-data-wrangler.readthedocs.io/en/stable/api.html#databases-amazon-redshift-postgresql-mysql)
+  - [Amazon Redshift](https://aws-data-wrangler.readthedocs.io/en/stable/api.html#amazon-redshift)
+  - [PostgreSQL](https://aws-data-wrangler.readthedocs.io/en/stable/api.html#postgresql)
+  - [MySQL](https://aws-data-wrangler.readthedocs.io/en/stable/api.html#mysql)
+  - [Amazon Timestream](https://aws-data-wrangler.readthedocs.io/en/stable/api.html#amazon-timestream)
   - [Amazon EMR](https://aws-data-wrangler.readthedocs.io/en/stable/api.html#amazon-emr)
   - [Amazon CloudWatch Logs](https://aws-data-wrangler.readthedocs.io/en/stable/api.html#amazon-cloudwatch-logs)
   - [Amazon QuickSight](https://aws-data-wrangler.readthedocs.io/en/stable/api.html#amazon-quicksight)

awswrangler/__metadata__.py

@@ -7,5 +7,5 @@
 
 __title__: str = "awswrangler"
 __description__: str = "Pandas on AWS."
-__version__: str = "1.10.1"
+__version__: str = "2.0.0"
 __license__: str = "Apache License 2.0"

docs/source/index.rst

@@ -27,17 +27,10 @@ Quick Start
     # Retrieving the data from Amazon Athena
     df = wr.athena.read_sql_query("SELECT * FROM my_table", database="my_db")
 
-    # Get Redshift connection (SQLAlchemy) from Glue and retrieving data from Redshift Spectrum
-    engine = wr.catalog.get_engine("my-redshift-connection")
-    df = wr.db.read_sql_query("SELECT * FROM external_schema.my_table", con=engine)
-
-    # Get MySQL connection (SQLAlchemy) from Glue Catalog and LOAD the data into MySQL
-    engine = wr.catalog.get_engine("my-mysql-connection")
-    wr.db.to_sql(df, engine, schema="test", name="my_table")
-
-    # Get PostgreSQL connection (SQLAlchemy) from Glue Catalog and LOAD the data into PostgreSQL
-    engine = wr.catalog.get_engine("my-postgresql-connection")
-    wr.db.to_sql(df, engine, schema="test", name="my_table")
+    # Get a Redshift connection from Glue Catalog and retrieving data from Redshift Spectrum
+    con = wr.redshift.connect("my-glue-connection")
+    df = wr.redshift.read_sql_query("SELECT * FROM external_schema.my_table", con=con)
+    con.close()
 
 Read The Docs
 -------------

docs/source/what.rst

@@ -1,8 +1,8 @@
 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 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 (**Amazon Redshift**, **AWS Glue**, **Amazon Athena**, **Amazon Timestream**, **Amazon EMR**, **Amazon QuickSight**, etc).
 
-Built on top of other open-source projects like `Pandas <https://github.com/pandas-dev/pandas>`_, `Apache Arrow <https://github.com/apache/arrow>`_, `Boto3 <https://github.com/boto/boto3>`_, `SQLAlchemy <https://github.com/sqlalchemy/sqlalchemy>`_, `Psycopg2 <https://github.com/psycopg/psycopg2>`_ and `PyMySQL <https://github.com/PyMySQL/PyMySQL>`_, it offers abstracted functions to execute usual ETL tasks like load/unload data from **Data Lakes**, **Data Warehouses** and **Databases**.
+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**.
 
 Check our `tutorials <https://github.com/awslabs/aws-data-wrangler/tree/master/tutorials>`_ or the `list of functionalities <https://aws-data-wrangler.readthedocs.io/en/stable/api.html>`_.

tests/test_metadata.py

@@ -2,7 +2,7 @@
 
 
 def test_metadata():
-    assert wr.__version__ == "1.10.1"
+    assert wr.__version__ == "2.0.0"
     assert wr.__title__ == "awswrangler"
     assert wr.__description__ == "Pandas on AWS."
     assert wr.__license__ == "Apache License 2.0"