---
title: "Top Techniques to Chain and Reuse Notebooks in Databricks Workflows"
description: "Explore the top techniques for reusing notebooks in Databricks, including %run, dbutils.notebook.run(), and workflow automation."
url: https://srinimf.com/2025/06/23/top-techniques-to-chain-and-reuse-notebooks-in-databricks-workflows/
date: 2025-06-23
modified: 2026-10-10
author: "Srini"
image: https://i0.wp.com/srinimf.com/wp-content/uploads/2025/06/technics-for-chaining-of-notebooks.png?fit=2240%2C1260&ssl=1
categories: ["databricks"]
tags: ["Data Engineering", "dbutils", "Modular Notebooks", "Notebook Chaining"]
type: post
lang: en
---

# Top Techniques to Chain and Reuse Notebooks in Databricks Workflows

***Unlocking Modular and Reusable Data Workflows** – Databricks is a powerful platform built for big data analytics and machine learning. *

Related[Databricks: Essential Interview Questions for Data Engineers](https://srinimf.com/2024/03/24/5-must-know-databricks-data-engineers-interview-questions/)

As projects grow in size, keeping your code modular, clean, and reusable becomes important. One best practice is **calling one notebook from another**, enabling you [to create](https://srinimf.com/2024/01/10/how-to-create-and-monitor-pipelines-azure-databricks/) reusable functions, ETL steps, or shared logic.

In this blog post, we will explore **six different ways to call or run one notebook from another in Databricks**, highlighting syntax, use cases, and caveats. These methods work across various languages like Python, SQL, and Scala in Databricks.

## 1. `%run` Magic Command (Most Common Way)

The `%run` command is the most widely used method for calling another notebook in Databricks.

```
%run /Users/srini@example.com/Utilities/Helpers
```

### ✅ Use case:

- Reusing shared functions or variables.
- Loading configuration or helper notebooks.
- Works with notebooks in the same workspace.

### ⚠️ Notes:

- `%run` executes all the code in the target notebook and loads variables/functions into your current namespace.
- It only works in interactive mode (not inside a job task).
- Cannot pass parameters directly.

## 2. `dbutils.notebook.run()` (Used in Workflows and Jobs)

This is the official way to call one notebook from another programmatically and with parameter passing.

```
result = dbutils.notebook.run("/Path/To/Notebook", timeout_seconds=60, arguments={"input": "value"})
```

### ✅ Use case:

- Executing notebooks as tasks in workflows.
- Passing and receiving parameters.
- Works in job contexts.

### ⚠️ Notes:

- Executes the notebook as a new job with a different context (like a subprocess).
- You can use `dbutils.notebook.exit("result")` to return a value from the child notebook.

## 3. Chaining Tasks Using Databricks Workflows (No Code Required)

Databricks Workflows (formerly Jobs) allow you to chain multiple notebooks visually or via JSON.

### Steps:

- Go to **Workflows > Create Job**.
- Add multiple notebooks as tasks.
- Define dependencies between them.

### ✅ Use case:

- Production pipelines.
- Low-code orchestration.
- Schedule multiple notebook runs sequentially or in parallel.

### ⚠️ Notes:

- Good for automation, but not useful for ad hoc reuse or conditional logic within notebooks.

## 4. `import` Shared Logic via Databricks Repos (Git Integration)

If your notebooks are version-controlled via Databricks Repos, you can import Python files or notebooks just like standard Python modules.

```
from my_utils.helpers import some_function
```

### ✅ Use case:

- Modular and testable Python code.
- Reusing logic across notebooks in Repos.
- Ideal for teams following software engineering best practices.

### ⚠️ Notes:

- Only works with `.py` files, not `.ipynb` directly.
- Requires Git integration and code organization.

## 5. REST API to Trigger Notebooks (External Triggering)

You can trigger a notebook remotely using Databricks REST API.

### Example:

Call this endpoint:

```
POST /api/2.0/jobs/run-now
```

With payload:

```
{
  "job_id": 12345,
  "notebook_params": {
    "param1": "value1"
  }
}
```

### ✅ Use case:

- Triggering notebooks from external systems like Airflow, Jenkins, or Lambda.
- Remote orchestration.

### ⚠️ Notes:

- Requires authentication (PAT token or Azure AD).
- You don’t get variable access; only logs or output status.

## 6. Import as Python Modules via `%pip install -e` (Advanced Use)

You can organize notebooks as Python packages inside Databricks Repos, and use `%pip install -e .` to install them in edit mode.

### Steps:

1. Place your reusable logic in a folder (e.g., `mylib`).
2. Add `__init__.py` to make it a package.
3. Install using `%pip install -e .`
4. Import and use:

```
from mylib.module import my_function
```

### ✅ Use case:

- Enterprise-grade modularity.
- Unit testing with `pytest`.
- Works across multiple notebooks and projects.

### ⚠️ Notes:

- Higher learning curve.
- Requires Repos and code structure discipline.

## Summary Table

| Method | Parameter Support | Namespace Shared | Works in Jobs | Notes |
| --- | --- | --- | --- | --- |
| `%run` | ❌ | ✅ | ❌ | Good for dev |
| `dbutils.notebook.run()` | ✅ | ❌ | ✅ | Best for production |
| Databricks Workflows | ✅ | ❌ | ✅ | Visual chaining |
| Git Repos + `import` | ✅ | ✅ | ✅ | Python only |
| REST API | ✅ | ❌ | ✅ | External control |
| `%pip install -e` + import | ✅ | ✅ | ✅ | Advanced packaging |

## Best Practices

- Use `%run` during development or quick experimentation.
- Use `dbutils.notebook.run()` for parameterized logic in production pipelines.
- Use Databricks Workflows for orchestration without writing orchestration code.
- Leverage Git and Python modules for enterprise-grade reusability.

## Final Thoughts

Databricks provides a variety of flexible ways to **reuse notebooks**, **build modular pipelines**, and **automate workflows**. By choosing the right method depending on your use case—development, production, or external orchestration—you can keep your codebase clean, DRY (Don’t Repeat Yourself), and maintainable.

If you’re working in a team or scaling your data platform, investing in reusable patterns like `dbutils.notebook.run()` or Git-based module imports can save countless hours and reduce bugs.

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