Merge branch 'update-readme' of https://github.com/astronomer/apache-…

…airflow-provider-transfers into update-readme

README.md

@@ -9,10 +9,10 @@ transfers made easy<br><br>
 
 [![CI](https://github.com/astronomer/apache-airflow-provider-transfers/actions/workflows/ci-uto.yaml/badge.svg)](https://github.com/astronomer/apache-airflow-provider-transfers)
 
-The **Universal Transfer Operator** allows data transfers between any supported source and target Datasets in [Apache Airflow](https://airflow.apache.org/). It offers a consistent agnostic interface, simplifying the users' experience, so they do not need to use specific providers or operators for transfers. The Astro Python SDK is maintained by [Astronomer](https://astronomer.io).
+The **Universal Transfer Operator** allows data transfers between any supported source and target Datasets in [Apache Airflow](https://airflow.apache.org/). It offers a consistent agnostic interface, simplifying the users' experience, so they do not need to use specific providers or operators for transfers. The Universal Transfer Operator is maintained by [Astronomer](https://astronomer.io).
 
-This ensures a consistent set of data providers that can read from and write to dataset. The Universal Transfer
-Operator can use the respective data providers to transfer between as a source and a destination. It also takes advantage of any existing fast and
+This ensures a consistent set of data providers that can read from and write to the dataset. The Universal Transfer
+Operator can use the respective data providers to transfer between a source and a destination. It also takes advantage of any existing fast and
 direct high-speed endpoints, such as Snowflake's built-in ``COPY INTO`` command to load S3 files efficiently into Snowflake.
 
 Universal transfer operator also supports the transfers using third-party platforms like Fivetran.
@@ -52,7 +52,7 @@ Steps:
 - Request destination dataset to ingest data from the file dataset.
 - Destination dataset request source dataset for data.
 
-This is a faster way for datasets of larger size as there is only one network call involved and usually the bandwidth between vendors is high. Also, there is no requirement for memory/processing power of the worker node, since data never gets on the node. There is significant performance improvement due to native transfers.
+This is a faster way to transfer datasets of larger size as there is only one network call involved and usually the bandwidth between vendors is high. Also, there is no requirement for memory/processing power of the worker node, since data never gets on the node. There is significant performance improvement due to native transfers.
 
 > **_NOTE:_**
    Native implementation is in progress and will be added in upcoming releases.