Doc updates (#336)

docs/comparison/taskflow_api.md

@@ -3,17 +3,17 @@
 For users that employ lots of Python functions in their DAGs, [TaskFlow API](https://www.astronomer.io/docs/learn/airflow-decorators/) represent a simpler way to transform functions into tasks, with a more intuitive way of passing data between them.
 They were  introduced in Airflow 2 as an alternative to Airflow [traditional operators](traditional_operators.md).
 
-Below, we illustrate how to represent an Airflow DAG using TaskFlow API and how to define the same DAG using
+The following section shows how to represent an Airflow DAG using TaskFlow API and how to define the same DAG using
 DAG Factory. Ultimately, both implementations use the same Airflow operators. The main difference is the language used
-to declare the workflow: while one uses Python, the other uses YAML.
+to declare the workflow: one uses Python and the other uses YAML.
 
 
 ## Goal
 
 Let's say we'd like to create a workflow that performs the following:
 
-1. Create a list of [PyPI](https://pypi.org/) projects to be analysed
-2. Fetch the [statistics](https://pypistats.org/) for each of these projects
+1. Create a list of [PyPI](https://pypi.org/) projects to be analysed.
+2. Fetch the [statistics](https://pypistats.org/) for each of these projects.
 3. Summarize the selected statistics as Markdown, using Python.
 
 We will implement all these steps using the Airflow `task` decorator, and the last task will generate a Markdown table similar to:
@@ -58,9 +58,8 @@ Both implementations accomplish the same goal and result in the expected Markdow
 
 ### Airflow Graph view
 
-As it can be seen in the screenshots below, both the DAG created using Python with standard Airflow and the
-DAG created using YAML and DAG Factory look identical, from a graph topology perspective, and also from the underlining
-operators being used.
+As shown in the screenshots below, both the DAG created using Python with standard Airflow and the
+DAG created using YAML and DAG Factory look identical, from a graph topology perspective, and also from the underlining operators being used.
 
 #### Graph view: Plain Airflow Python DAG
 
@@ -72,7 +71,7 @@ operators being used.
 
 ### Airflow Dynamic Task Mapping
 
-In both workflows, we are generating dynamically a task for each PyPI repo.
+In both workflows, we are dynamically generating a task for each PyPI repo.
 
 #### Mapped Tasks: Plain Airflow Python DAG
 
@@ -99,7 +98,6 @@ From an Airflow UI perspective, the content displayed in the "Code" view is the
 
 ![alt text](../static/example_pypi_stats_dagfactory_code.png "YAML DAG code visualization")
 
-To overcome this limitation, DAG Factory appends the YAML content to the DAG Documentation so users can better troubleshoot
-the DAG:
+To overcome this limitation, DAG Factory appends the YAML content to the DAG Documentation so users can better troubleshoot the DAG:
 
 ![alt text](../static/example_pypi_stats_dagfactory_docs.png "YAML DAG docs visualization")

docs/comparison/traditional_operators.md

@@ -7,7 +7,7 @@ Most of the Airflow providers come with built-in traditional operators. Some exa
 
 Below, we illustrate how to represent an Airflow DAG using traditional operators and how to define the same DAG using
 DAG Factory. Ultimately, both implementations use the same Airflow operators. The main difference is the language used
-to declare the workflow: while one uses Python, the other uses YAML.
+to declare the workflow: one uses Python and the other uses YAML.
 
 
 ## Goal
@@ -61,7 +61,7 @@ Both implementations accomplish the same goal and result in the expected Markdow
 
 ### Airflow Graph view
 
-As it can be seen in the screenshots below, both the DAG created using Python with standard Airflow and the
+As shown in the screenshots below, both the DAG created using Python with standard Airflow and the
 DAG created using YAML and DAG Factory look identical, from a graph topology perspective, and also from the underlining
 operators being used.
 

docs/getting-started/quick-start-airflow-standalone.md

@@ -11,7 +11,7 @@ The minimum requirements for **dag-factory** are:
 
 ## Step 1: Create a Python Virtual Environment
 
-First, create and activate a virtual environment:
+Create and activate a virtual environment:
 
 ```commandline
 python3 -m venv dagfactory_env
@@ -20,7 +20,7 @@ source dagfactory_env/bin/activate
 
 ## Step 2: Install Apache Airflow
 
-Next, you'll need to install [Apache Airflow®](https://airflow.apache.org). Follow these steps:
+Install [Apache Airflow®](https://airflow.apache.org):
 
 1. Create a directory for your project and navigate to it:
 
@@ -43,7 +43,7 @@ Next, you'll need to install [Apache Airflow®](https://airflow.apache.org). Fol
 
 ## Step 3: Install DAG Factory
 
-Now, install the DAG Factory library in your virtual environment:
+Install the DAG Factory library in your virtual environment:
 
 ```commandline
 pip install dag-factory

docs/getting-started/quick-start-astro-cli.md

@@ -11,7 +11,7 @@ The minimum requirements for **dag-factory** are:
 
 ## Step 1: Initialize Airflow Project
 
-Start by creating a new directory and initializing your Astro CLI project:
+Create a new directory and initialize your Astro CLI project:
 
 ```commandline
 mkdir dag-factory-quick-start && cd dag-factory-quick-start
@@ -23,7 +23,7 @@ This will set up the necessary Airflow files and directories.
 
 ## Step 2: Install DAG Factory
 
-To install DAG Factory in your Airflow environment:
+Install DAG Factory in your Airflow environment:
 
 1. Add dag-factory as a dependency to the `requirements.txt` file created during the project initialization.