Perfect — this transcript is about Databricks Notebook Orchestration and how to parameterize/run

1. Introduction

Databricks notebooks can be parameterized and orchestrated like workflows.
You can:

• Pass parameters to notebooks using widgets.
• Trigger one notebook from another using dbutils.notebook.run().
• Capture results (exit status, record counts, etc.).
• Schedule notebooks as jobs.

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2. Setup: Parent vs Child Notebook

• Child Notebook: Does the main processing (e.g., read employees, filter, write Delta tables).
• Parent Notebook: Calls child notebooks with parameters and orchestrates runs.

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3. Step 1: Parameterizing a Child Notebook

Inside the child notebook (write_emp_data):

# Step 1: Create a widget for parameter input
dbutils.widgets.text("DPT", "", "Department Name")

# Step 2: Fetch the widget value
dept = dbutils.widgets.get("DPT")
print(f"Department passed: {dept}")

✅ This creates a text box at the top of the notebook where you can pass department values (like Sales, Office).

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4. Step 2: Process Data in the Child Notebook

# Read employee dataset
df = spark.read.csv("dbfs:/mnt/emp/employee.csv", header=True)

# Filter by department and active records
df_filtered = df.where(
    (df["Department"] == dept.upper()) & (df["Active_Record"] == "1")
)

# Write data dynamically into a Delta table
if df_filtered.count() > 0:
    table_name = f"dev.bronze.d_{dept.lower()}"
    df_filtered.write.mode("overwrite").saveAsTable(table_name)
    print(f"Data written for {dept}")
else:
    print(f"No data for {dept}")

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5. Step 3: Return Results to Parent Notebook

Child notebook can return a result:

# Pass the record count back to parent
count = df_filtered.count()
dbutils.notebook.exit(str(count))

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6. Step 4: Orchestrating from the Parent Notebook

In parent notebook (run_emp_data):

# Run child notebook with parameters
result = dbutils.notebook.run(
    "write_emp_data",  # Notebook path or name
    timeout_seconds=600,
    arguments={"DPT": "Sales"}
)

print(f"Child notebook returned {result} records")

You can loop through multiple departments:

departments = ["Sales", "Office", "Production"]

for dept in departments:
    result = dbutils.notebook.run(
        "write_emp_data",
        timeout_seconds=600,
        arguments={"DPT": dept}
    )
    print(f"{dept}: {result} records processed")

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7. Step 5: Scheduling the Notebook

Databricks UI lets you schedule notebooks without external tools:

• In the parent notebook, click Schedule (top-right).
• Give the job a name.
• Choose cluster (existing or new).
• Set frequency:
  • Daily, weekly, or custom cron syntax (e.g., 0 8 * * * for 8AM daily).
• (Optional) Configure alerts: email/Slack on success/failure.
• Add parameters (widgets like DPT).

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8. Key Commands Recap

• Create widget: dbutils.widgets.text("DPT", "", "Department")
• Get widget value: dept = dbutils.widgets.get("DPT")
• Run notebook: dbutils.notebook.run("child_nb", 600, {"param": "value"})
• Return value from notebook: dbutils.notebook.exit("some_value")

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✅ Outcome: You now have a reusable system where a parent notebook can call child notebooks with parameters, orchestrate workflows, and schedule them automatically in Databricks.

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