Integration with BigQuery empowers organizations to conduct advanced real-time analyses of large datasets. With Morph, users can easily extract data through SQL and leverage Python and MDX for versatile visualization and automation. This approach provides secure and high-speed insights, driving more impactful, data-driven decisions across the entire team.

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_users_list",
    connection="CONNECTION_NAME"
  )
}}
SELECT id, name, email, age
FROM users
WHERE created_at >= '2024-01-01'
ORDER BY created_at 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_users_list")
def visualize_users(context):
    df = context.data["get_users_list"].groupby("age").size().reset_index(name="count")
    fig = px.bar(df, x="age", y="count")
    return fig

Markdown

export const title = "Starter App"

# Starter App

This is a starter app.

## Data

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