---
title: "How to define an AWS Athena view using Airflow"
description: "How to use the AWSAthenaOperator"
author: "Bartosz Mikulski"
author_bio: "Principal AI Engineer & MLOps Architect. I bridge the gap between \"it works in a notebook\" and \"it works for 200 million users.\""
author_url: https://mikulskibartosz.name
author_linkedin: https://www.linkedin.com/in/mikulskibartosz/
author_github: https://github.com/mikulskibartosz
canonical_url: https://mikulskibartosz.name/define-athena-view-using-airflow
---

In this brief tutorial, I will show how to define an AWS Athena view using Airflow. We will need two things:

* AWSAthenaOperator
* the SQL query that defines the view

Let's start with the query. To define the view, we have to call the `CREATE VIEW` statement. However, to make it work flawlessly in Airflow, we should make the statement pass even if the view already exists, so I suggest using `CREATE OR REPLACE VIEW`. Here is an example SQL query that creates a view:

```sql
create_view_sql = 'CREATE OR REPLACE VIEW the_view_name AS SELECT t1.* FROM some_table t1;'
```

Of course, such a view makes no sense, but it is good enough to use in a tutorial.

Now, we have to import the `AWSAthenaOperator`:

```python
from airflow.contrib.operators.aws_athena_operator import AWSAthenaOperator
```

After that, we can create a new instance of the operator and add it to a `dag`. Note that I have to define the AWS connection id, which refers to a connection configured in Airflow and the database in which I want to create the view. In addition to that, I have to pass the S3 location where I want to store Athena queries' results because Athena (being a Presto-based service) stores the results in files:

```python
create_view = AWSAthenaOperator(
    task_id='create_the_view',
    query=create_view_sql,
    aws_conn_id='aws_connection_id',
    database='athena_database',
    output_location='the_output_location_of_athena_queries',
    dag=dag
)
```