In this session, I will guide you step by step through the process of working with Notebooks and extracting data from APIs. Here's the approach we'll take:

Introduction to Notebooks and APIs: I will explain how Notebooks in Fabric provide a fast, efficient solution for data transformation and how APIs can serve as valuable data sources.

Setting Up the Environment: We'll start by installing and importing the essential libraries needed for working with PySpark and interacting with APIs.

Extracting Data from APIs: I'll demonstrate how to fetch data from an API, process it, and bring it into a DataFrame for further manipulation.

Creating a User Defined Function (UDF): I'll show you how to create a custom UDF in PySpark to handle more advanced data transformations. We will walk through the steps of defining the function, converting it into a UDF, and applying it to your DataFrame.

Complex Data Transformations: Finally, I will guide you on how to use UDFs to perform more complex transformations on your data, making it ready for other Fabric tasks.

Copilot: Understand how to use Copilot in notebooks for Data Engineering workloads to generate code snippets, provide explanation for existing code, suggest data visualizations, suggest analytical machine learning models, and more.

By the end of this session, you’ll have a clear understanding of how to leverage Notebooks and APIs in Fabric to perform complex data transformations with PySpark and UDFs.

This session is suitable for beginners, although basic knowledge of Azure Synapse Analytics or Microsoft Fabric is recommended. While the session is primarily aimed at data engineers, data architects and stakeholders are also welcome to attend.