Integration with Amazon Athena provides a serverless approach for querying large-scale data quickly and flexibly. With Morph, users can run SQL queries to retrieve crucial information instantly, then leverage Python and MDX for customization and advanced analytics. This streamlined process simplifies data ingestion, visualization, and reporting, driving more efficient data-driven collaboration across teams.

Set up connection information

Please refer to the DB/SaaS Connection to register the SQL Connection.

Example of SQL query execution

You can create .sql files and execute SQL queries on the connected database. Use the config function to select the connection you want to use from the available SQL Connections. Replace the value of connection with the name of the SQL Connection you created.

{{
  config(
    name="get_access_count",
    connection="CONNECTION_NAME"
  )
}}
SELECT
  status_code,
  COUNT(*) AS access_count
FROM
  your_alb_log_table_name
GROUP BY
  status_code
ORDER BY
  access_count DESC;

Use in SQL and Python

The SQL queries you create can be called from Python and MDX files.

Python

@morph.func
@morph.load_data("get_access_count")
def visualize_access_count(context):
    df = context.data["get_access_count"]
    fig = px.bar(df, x="status_code", y="access_count")
    return fig

Markdown

export const title = "Starter App"

# Starter App

This is a starter app.

## Data

<Grid cols="2">
  <div>
    <DataTable loadData="get_access_count" height={300} />
  </div>
  <div>
    <Embed loadData="visualize_access_count" height={300} />
  </div>
</Grid>