ephraimbuddy commented on a change in pull request #272:
URL: https://github.com/apache/airflow-site/pull/272#discussion_r463300876
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File path: landing-pages/site/content/en/blog/apache-airflow-for-new-comers.md
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+---
+title: "Apache Airflow For Newcomers"
+linkTitle: "Apache Airflow For Newcomers"
+author: "Ephraim Anierobi"
+twitter: "ephraimbuddy"
+github: "ephraimbuddy"
+description: ""
+tags: []
+date: "2020-07-16"
+draft: false
+---
+
+Apache Airflow is a platform to programmatically author, schedule, and monitor
workflows.
+A workflow is a sequence of tasks that processes a set of data. You can think
of workflow as the
+path that describes how tasks go from being undone to done. Scheduling, on the
other hand, is the
+process of planning, controlling, and optimizing when a particular task should
be done.
+
+### Authoring Workflow in Apache Airflow.
+Airflow makes it easy to author workflows using python scripts. A DAG(Directed
Acyclic Graph)
+represents a workflow in Airflow. It is a collection of tasks in a way that
shows each task's
+relationships and dependencies. You can have as many DAGs as you want, and
Airflow will execute
+them in line with the task's relationships and dependencies. If task B depends
on the successful
+execution of another task A, it means airflow will run task A and only run
task B after task A.
+This dependency is very easy to express in Airflow. For example, the above
scenario is expressed as
+```python
+task_A >> task_B
+```
+Also equivalent to
+```python
+task_A.set_downstream(task_B)
+```
+
+That helps airflow to know that it needs to execute task A before task B.
+Let us now discuss the architecture of Airflow that makes scheduling,
executing, and monitoring of
+workflow an easy thing.
+
+### Scheduler
+The scheduler is the component that monitors DAGs and triggers those tasks
whose dependencies have
+been met. It watches over the DAG folder, checking the tasks in each DAG and
triggers them once they
+are ready. It accomplishes these by reading the metadata database to check the
status of each task and
+decides what needs to be done. The metadata database is where the status of
all tasks are recorded.
+The status can be one of running, success, failed, etc.
+
+In the breeze environment, the scheduler is started by running the command
`airflow scheduler`.
+
+### Executor
+Executors are responsible for running tasks. They work with the scheduler to
get information about
+what resources are needed to run a task as the task is queued.
+
+By default, airflow uses the SequentialExecutor. However, this executor is
limited and it is the only
+executor that can be used with SQLite.
+
+There are many other executors, the difference is on the resources they have
and how they choose to
+use the resources
+
+### Webserver
+The webserver is the web interface(UI) for Airflow. The UI is feature-rich. It
makes it easy to
+monitor and troubleshoot DAGs and Tasks.
+
+There are many actions you can perform on the UI. You can trigger a task,
monitor the execution
+including the duration of the task. The UI makes it possible to view the
task's dependencies in a
+tree view and graph view. You can view task logs in the UI.
+
+The web UI is started with the command `airflow webserver` in the breeze
environment.
+
+### Backend
+By default, Airflow uses the SQLite backend for storing the configuration
information, DAG states,
Review comment:
I think it is both the breeze default and the default every time breeze
is used for the first time unless configured to use Postgres or MySQL.
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