Integrate Apache Airflow with Tiger Cloud

Apache Airflow is a platform to programmatically author, schedule, and monitor workflows. Integrate Apache Airflow with Tiger Cloud and create a data pipeline

Apache Airflow® is a platform created by the community to programmatically author, schedule, and monitor workflows.

A DAG (Directed Acyclic Graph) is the core concept of Airflow, collecting Tasks together, organized with dependencies and relationships to say how they should run. You declare a DAG in a Python file in the $AIRFLOW_HOME/dags folder of your Airflow instance.

This page shows you how to use a Python connector in a DAG to integrate Apache Airflow with a .

Prerequisites

To follow the steps on this page:

• Create a target with time-series and analytics enabled.

  You need your connection details. This procedure also works for .

• Install Python3 and pip3

• Install Apache Airflow

  Ensure that your Airflow instance has network access to .

This example DAG uses the company table you create in Optimize time-series data in hypertables

Install python connectivity libraries

To install the Python libraries required to connect to :

Create a connection between Airflow and your

In your Airflow instance, securely connect to your :

Exchange data between Airflow and your

To exchange data between Airflow and your :

You have successfully integrated Apache Airflow with and created a data pipeline.