Load data with a Python script in Bold Data Hub

The Bold Data Hub PythonScript connector runs a Python script to load data into Bold Reports^®. A PythonScript is a .py file with Python instructions for tasks such as automation, data processing, or analysis.

Example Configuration

The following Bold Data Hub YAML template shows a complete PythonScript extractor configuration:

version: 1.0.1
encrypt_credentials: false
union_all_tables: true
add_dbname_column: false
use_snake_casing: true
plugins:
  extractors:
  - name: pyscript1
    connectorname: PythonScript
    schemaname:
    config:
      filePath: C:\BoldServices\app_data\elt\connectors\csvreaders.py
      properties:
    metadata:
    select:

How do I connect a Python script in Bold Data Hub?

You connect a Python script by creating a pipeline in Bold Data Hub, adding the PythonScript template, and uploading your .py file.

Click the Data Hub icon on the Navigation Pane.
Click Add Pipeline and enter a name for the new pipeline.
Select the newly created pipeline and add the PythonScript template.
In your Python script, make sure you have a data frame object. Add the following line after the dataframe object that is moved to tables using Bold ETL:
```
pipeline.run(yourdataframename, table_name="yourtablename")
```
Replace yourdataframename with the dataframe name and yourtablename with the table name you want in your destination database.
Click the Upload File button to upload your Python file.
Copy the file path and paste it into the filePath property.
Click Save and choose the destination to save the pipeline.

Creating a pipeline in Bold Data Hub automatically creates a data source in Bold Reports^®. The Bold Reports^® data source is a live data source to the destination database used in Bold Data Hub.

PythonScript- Reports

How do I schedule the PythonScript pipeline?

You schedule the PythonScript pipeline in Bold Data Hub from the schedules tab, or you run it on demand.

To configure interval-based scheduling, click the schedules tab, select the created pipeline, click the schedule icon, and configure the schedule.
For an on-demand refresh, click the Run Now button.
Check the schedule history with the history option and the logs.
Click Logs to see whether the run is completed and the data source is created in Bold Reports^®.
Click the Edit DataSource option to view the created tables.

Frequently asked questions

What must my Python script contain for Bold Data Hub?

Your script needs a data frame object, followed by a pipeline.run(yourdataframename, table_name="yourtablename") line that moves it to a table.

How does Bold Data Hub find my Python file?

Upload the file with the Upload File button, then copy its file path into the filePath property.