---
title: Integrate Apache Airflow with Tiger Cloud | Tiger Data Docs
description: Author, schedule, and monitor workflows to orchestrate your data pipelines
---

A [DAG (Directed Acyclic Graph)](https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html#dags) is the core concept of Airflow, collecting [Tasks](https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/tasks.html) 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 Tiger Cloud service.

## Prerequisites

To follow the procedure on this page you need to:

- Create a [target Tiger Cloud service](/docs/get-started/quickstart/create-service/index.md).

  This procedure also works for [self-hosted TimescaleDB](/docs/get-started/choose-your-path/install-timescaledb/index.md).

* Install [Python3 and pip3](https://docs.python.org/3/using/index.html)

* Install [Apache Airflow](https://airflow.apache.org/docs/apache-airflow/stable/start.html)

  Ensure that your Airflow instance has network access to Tiger Cloud.

This example DAG uses the `company` table you create in [Optimize time-series data in hypertables](/docs/learn/tiger-cloud/tiger-cloud-essentials#optimize-time-series-data-in-hypertables-with-hypercore/index.md)

## Install python connectivity libraries

To install the Python libraries required to connect to Tiger Cloud:

1. **Enable PostgreSQL connections between Airflow and Tiger Cloud**

   Terminal window

   ```
   pip install psycopg2-binary
   ```

2. **Enable PostgreSQL connection types in the Airflow UI**

   Terminal window

   ```
   pip install apache-airflow-providers-postgres
   ```

## Create a connection between Airflow and your service

In your Airflow instance, securely connect to your Tiger Cloud service:

1. **Run Airflow**

   On your development machine, run the following command:

   Terminal window

   ```
   airflow standalone
   ```

   The username and password for Airflow UI are displayed in the `standalone | Login with username` line in the output.

2. **Add a connection from Airflow to your Tiger Cloud service**

   1. In your browser, navigate to `localhost:8080`, then select `Admin` > `Connections`.
   2. Click `+` (Add a new record), then use your [connection info](/docs/integrate/find-connection-details/index.md) to fill in the form. The `Connection Type` is `Postgres`.

## Exchange data between Airflow and your service

To exchange data between Airflow and your Tiger Cloud service:

1. **Create and execute a DAG**

   To insert data in your Tiger Cloud service from Airflow:

   1. In `$AIRFLOW_HOME/dags/timescale_dag.py`, add the following code:

      ```
      from airflow import DAG
      from airflow.operators.python_operator import PythonOperator
      from airflow.hooks.postgres_hook import PostgresHook
      from datetime import datetime


      def insert_data_to_timescale():
          hook = PostgresHook(postgres_conn_id='the ID of the connenction you created')
          conn = hook.get_conn()
          cursor = conn.cursor()
          """
            This could be any query. This example inserts data into the table
            you create in:


            https://www.tigerdata.com/docs/getting-started/latest/try-key-features-timescale-products/#optimize-time-series-data-in-hypertables-with-hypercore
           """
          cursor.execute("INSERT INTO crypto_assets (symbol, name) VALUES (%s, %s)",
           ('NEW/Asset','New Asset Name'))
          conn.commit()
          cursor.close()
          conn.close()


      default_args = {
          'owner': 'airflow',
          'start_date': datetime(2023, 1, 1),
          'retries': 1,
      }


      dag = DAG('timescale_dag', default_args=default_args, schedule_interval='@daily')


      insert_task = PythonOperator(
          task_id='insert_data',
          python_callable=insert_data_to_timescale,
          dag=dag,
      )
      ```

      This DAG uses the `company` table created in [Create regular PostgreSQL tables for relational data](/docs/learn/tiger-cloud/tiger-cloud-essentials#optimize-time-series-data-in-hypertables-with-hypercore/index.md).

   2. In your browser, refresh the Airflow UI.

   3. In `Search DAGS`, type `timescale_dag` and press ENTER.

   4. Press the play icon and trigger the DAG:

      ![Apache Airflow DAG results showing daily ETH transaction volume](/docs/_astro/integrations-apache-airflow.CuAODU3S_Ro4x1.webp)

2. **Verify that the data appears in Tiger Cloud**

   1. In [Tiger Console](https://console.cloud.tigerdata.com/), navigate to your service and click `SQL editor`.

   2. Run a query to view your data. For example: `SELECT symbol, name FROM company;`.

      You see the new rows inserted in the table.

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