Hi @ToReSa, If you just want to execute the notebook, calling another notebook would be easier. You can even exchange some data between the notebooks.

But if you specifically want to pick each SQL from one notebook and execute it in another notebook, you should go with this approach:-

• Export notebook A as JSON: Export the notebook to JSON for programmatic access.

• Extract SQL Commands: Read the JSON file in notebook B and extract SQL commands from each cell.

• Execute SQL Commands: Run each SQL command in notebook B and save results to CSV files.

Here is the sample script:-

import json
import requests
import pandas as pd

# Export notebookA as JSON
response = requests.get(
    'https://your-databricks-instance/api/2.0/workspace/export',
    headers={'Authorization': 'Bearer your_databricks_token'},
    params={'path': '/path/to/notebookA', 'format': 'SOURCE'}
)
notebookA_content = response.json()

# Extract SQL commands
cells = notebookA_content['content']['cells']
sql_commands = [cell['source'] for cell in cells if cell['cell_type'] == 'code']

# Execute and export results
for i, sql in enumerate(sql_commands):
    result = spark.sql(sql)
    result_df = result.toPandas()
    result_df.to_csv(f'result_{i}.csv', index=False)

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