Welcome to Apache Airflow UI. After watching this video, you will be able
to: Identify current DAGs in your environment. List different ways to visualize a specific
DAG. Review the code that defines your DAG. Analyze the duration of each task in your
DAG over multiple runs. And, select context metadata for any task
instance. ​Here's what the landing page for the Apache
Airflow User Interface looks like in your browser. It defaults to the 'DAGs View,' which
is a table containing data about each DAG in your environment. Each row displays interactive information
about a DAG in your environment, such as Each DAG's name; Its run schedule, in this case in the crontab
format; The DAG's owner. This one is a built-in
example from Airflow; The status of the tasks from the current or
most recent DAG run; The status of all previous DAG runs; Plus a collection of quick-links to drill
down into more information related to the DAG. On the far left, you can drill down to access
more interactive details, and in the next column you can toggle
to pause a DAG. All of the DAGs here are currently not running. You can visualize DAGs in several ways, as
follows. Start by clicking on the name of the DAG you
want to visualize. This DAG, called ‘simple example’, happens to be running in production,
as you can see here by the ‘on’ button. Your DAG's 'Tree View' opens by default when
you click on its name. It shows a timeline of the status of your
DAG and its tasks for each run. Here, you can select the base-date and the
number of runs to display. Each status is color-coded according to the
legend, displayed here. You can also hover your mouse pointer over
any task in the timeline to view more information about it. Here's another DAG, this time displayed in
'Graph View'. At the bottom of the screen you can see your DAG’s tasks and dependencies.
Each task is color coded by its operator type, according to this legend . Here, you can filter your view by toggling
these status-option buttons. The 'Task Instance Context Menu' can be accessed
from any of these kinds of views, that is, views that display task instances for your
DAG. This menu allows you, for example, to drill
down on a selected task instance to view and edit many details about it, or to view
the log file for your task. By clicking on the Code button, you can also
view the complete Python source code that defines your DAG. Here we see the building blocks of your DAG,
for example, the library imports, and the individual task definitions, which invoke
Bash Operators in this case. By clicking on 'Task Duration', You can view
a timeline chart of your DAG's task durations to see how they have been performing.  Here
you can toggle the tasks that you wish to highlight the last N runs for. In this video, you learned that: Apache Airflow has a rich UI that simplifies
working with data pipelines. You can visualize your DAG in several informative
ways, including both graph and tree mode. You can also review the Python code that
originally defined your DAG. You can analyze the duration of each task
in your DAG over multiple runs. And finally, you can select context metadata
for any task instance.