Data Engineering with Apache Spark in Fabric

Modern data workloads require massive scaling and unified platforms to turn raw data into actionable insights. Fabric offers a powerful, integrated environment where Apache Spark handles large-scale data processing with ease. This text-only course guides you through the foundational concepts of distributed computing and Spark architecture within Fabric. You will learn how to ingest, transform, and analyze big data using Spark notebooks, prepare data for downstream analytics, and optimize your workflows for maximum efficiency. What you'll learn: 1. Understand the core architecture of Apache Spark and how it integrates into the Fabric ecosystem. 2. Write and execute Spark code in interactive notebooks using PySpark and Spark SQL. 3. Ingest and transform large datasets from diverse sources into optimized Delta Lake tables. 4. Apply modern data engineering practices, including basic performance tuning and caching strategies. 5. Configure Spark sessions and manage compute resources effectively within your workspace. 6. Build scalable data pipelines to automate your analytical workflows. You will start with the fundamental terminology of distributed clusters and lakehouse architecture before moving on to written explanations of code examples, data manipulation techniques, and best practices for writing clean, efficient Spark jobs. This course is designed for aspiring data engineers, analysts, and beginners looking to transition into big data technologies. No prior experience with Spark or Fabric is required. Start reading today to unlock the power of large-scale data processing in the cloud.