Integration with MySQL empowers teams to effortlessly obtain and manage data, ensuring up-to-date insights for informed decision-making. By writing straightforward SQL queries, data can be quickly retrieved and analyzed, enabling efficient workflows. MDX-driven dashboards further simplify visualization, while Python scripts unlock deeper customization and streamlined automation. From real-time reporting to robust analytics, Morph leverages MySQL’s strengths to help users optimize their data processes and collaborate more effectively across the organization.

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>