Fabric Notebooks for Data Engineering and PySpark

Modern data engineering requires scalable, cloud-based environments to process massive datasets efficiently. Fabric notebooks combine the power of Apache Spark with a collaborative workspace to streamline your entire data preparation pipeline. This text-based course guides you from the absolute basics of cloud data engineering to writing production-ready transformation scripts. You will understand how to connect to lakehouses, query data using Spark SQL, and apply complex transformations using PySpark, all while following modern data practices. What you'll learn: 1. Understand the core architecture of Fabric notebooks and the Delta lakehouse format. 2. Write efficient Spark SQL queries to filter, aggregate, and join large datasets. 3. Apply PySpark DataFrames to perform advanced programmatic data transformations. 4. Configure connections to various data stores, including lakehouses and warehouses. 5. Implement modern data engineering best practices, including schema enforcement and clean code structure. 6. Optimize notebook performance for handling large-scale data processing tasks. The course begins with foundational concepts, introducing you to the Fabric workspace and basic notebook operations. You will then progress through step-by-step written tutorials and structured code exercises that teach you how to read, transform, and write data. This course is designed for aspiring data engineers, data analysts, and database administrators who want to learn cloud data transformation. No prior experience with Fabric or Spark is required, though a basic understanding of SQL and Python is helpful. Start reading today to master data transformation with Fabric notebooks.