Databricks Foundations: Data Engineering and Lakehouse Basics

Organizations today struggle to manage separate data lakes and data warehouses, leading to complex pipelines and fragmented governance. The Lakehouse architecture solves this by combining the best of both worlds into a single, unified platform. In this course, you will transition from a beginner to understanding how to orchestrate end-to-end data workloads. You will explore how Databricks manages storage, compute, and governance to streamline data engineering, analytics, and machine learning workflows. What you'll learn: - Understand the foundational concepts of the Lakehouse architecture and Delta Lake storage. - Build scalable data pipelines using the Medallion architecture from raw ingestion to clean business tables. - Configure compute clusters and automate data pipelines using built-in workflows. - Query data lakes using standard SQL and build analytical dashboards within Databricks SQL. - Manage data science lifecycles and track machine learning models using MLflow. - Apply modern data governance principles with Unity Catalog to secure your organization's data assets. The course starts with essential definitions and core architecture concepts before guiding you through data transformation, pipeline automation, and model tracking. You will learn through clear written explanations, conceptual breakdowns, and practical code snippets. This text-based course is designed for aspiring data engineers, analysts, and beginners who want to understand the modern data ecosystem. No prior experience with Databricks is required. Start reading today to build a strong foundation in modern data lakehouse technologies.