Fabric Notebooks Overview

In this video, Zane Goodman provides an overview of Fabric Notebooks, exploring their capabilities, features, and potential applications for data engineers and data scientists. In this post, we will summarize key takeaways from the video, breaking down the fundamental aspects of using Fabric Notebooks for data-related tasks.

What Are Fabric Notebooks?

Fabric Notebooks are tools designed to help data engineers and data scientists work with large datasets and perform a variety of tasks, from data ingestion and preparation to running machine learning experiments. These notebooks are used for:

• Data ingestion, preparation, and transformation (by data engineers)
• Creating and deploying machine learning models (by data scientists)
• Tracking experiments and managing models

Key Features of Fabric Notebooks

Fabric Notebooks offer a variety of features designed to enhance productivity and simplify complex data tasks:

• Zero setup effort: Start using Fabric Notebooks without worrying about complex configurations.
• Low-code experiences: Simplify data exploration and processing with intuitive user interfaces.
• Built-in enterprise security: Secure data processing with robust security features.
• Support for multiple data formats: Handle CSV, JSON, Delta Lake, and more with Spark capabilities.
• Enhanced authoring and data visualization: Create meaningful visualizations and insights within the notebook itself.

How to Run a Fabric Notebook

Running a notebook in Fabric is flexible, with three main options available:

1. Interactive: Manually run the notebook under the user's security context.
2. Pipeline: Execute the notebook within a pipeline, with the pipeline owner's security context.
3. Scheduled: Set up a schedule to run the notebook at specific times, using the scheduler's security context.

Creating a New Notebook in Fabric

Zane walks viewers through the process of creating a new notebook within the Fabric workspace. Notebooks can be created via:

• Workspace menus under the "New Item" section
• Workload selectors for data engineering or data science
• Importing pre-existing notebooks into the platform

Working with Data in Fabric Notebooks

In this segment, Zane demonstrates how to upload and work with data within the notebook interface:

• Uploading Files: Directly upload files under 100MB or connect to Lakehouse or Warehouse folders for data storage.
• Adding Code and Markdown Cells: Code cells allow for programming, while Markdown cells provide space for documentation.

Running and Debugging Notebooks

The Home tab in Fabric Notebooks provides several options to run and debug your notebooks:

• Save and Download: Save the notebook or download it for offline use.
• Sensitivity Labels: Add sensitivity labels for better organization.
• Run Options: Run all cells, or only specific ones above or below a given cell.

Collaborating and Documenting Work

Documentation is a vital aspect of any data project. Fabric Notebooks allow collaboration through:

• Markdown: Easily document your work, making it clear and understandable for your future self or team members.
• Table of Contents: Automatically generated from Markdown cells to help navigate large notebooks.
• Co-Pilot: Use AI assistance for code troubleshooting and finding relevant libraries.

Writing and Managing Data in Fabric Notebooks

Zane demonstrates how to manipulate and save data within a notebook. Common operations include:

• Reading Data: Load data into a DataFrame for manipulation.
• Modifying Data: Perform transformations such as renaming columns or applying functions.
• Saving Data: Save your results to new files, overwrite existing ones, or append data.
• Writing to Lakehouse: Store cleaned data as tables in Delta format for easier analysis.

Conclusion

Fabric Notebooks are a powerful tool for data engineers and data scientists, offering everything from data manipulation to machine learning model tracking and deployment. With intuitive interfaces, robust security, and fast performance, they provide a seamless experience for anyone working with large datasets or complex data tasks.

Don't forget to check out the Pragmatic Works' on-demand learning platform for more insightful content and training sessions on Fabric and other Microsoft applications. Be sure to subscribe to the Pragmatic Works YouTube channel to stay up-to-date on the latest tips and tricks.