Enterprise Data Engineering and AI with Databricks

Modern organizations rely on robust data pipelines to power their artificial intelligence and analytics initiatives. This text-based course guides you through the fundamentals of enterprise data engineering and AI integration using Databricks. Through clear written explanations and structured code walkthroughs, you will transition from understanding basic cloud data concepts to building reliable, production-ready pipelines. You will learn how to unify your data storage, processing, and machine learning workflows in a single, cohesive environment. What you'll learn: Understand the core principles of lakehouse architecture and how it unifies data warehousing and data lakes; Build and orchestrate data pipelines using Delta Live Tables for reliable, incremental data processing; Apply Delta Lake features to ensure data quality, ACID transactions, and schema enforcement; Integrate generative AI and machine learning models into your data engineering workflows; Configure basic MLOps practices, including model registry and tracking, within the Databricks ecosystem; Implement data governance and security best practices for enterprise-level compliance. The course begins with foundational concepts of data lakehouses and cloud data storage before moving into hands-on pipeline creation. You will then explore advanced topics like real-time data processing, machine learning integration, and enterprise governance. This course is designed for aspiring data engineers, analysts, and developers who want to build a solid foundation in modern data architecture. No prior experience with Databricks is required, though a basic understanding of SQL or Python is helpful. Start your journey into enterprise data engineering and build the skills to power modern AI systems.