Introduction to the Databricks Lakehouse Architecture

This course will teach you how to leverage the Databricks Lakehouse platform to build scalable, reliable, and unified data solutions for analytics, machine learning, and real-time processing.

What you'll learn

Traditional data storage solutions like data lakes and data warehouses offer scalability or reliability but not both. This hinders an organization’s ability to manage and analyze data effectively. In this course, Introduction to the Databricks Lakehouse Architecture, you’ll gain the ability to leverage the Databricks Lakehouse platform to create unified, scalable, and efficient data solutions.

First, you’ll explore the foundational concepts of the Lakehouse architecture, its advantages over traditional data lakes and data warehouses, and its core components, including Delta Lake, Spark, Databricks SQL, and MLflow. Next, you’ll discover how to work with the Lakehouse architecture using Delta Lakes, by querying structured and unstructured data, and managing machine learning workflows using MLflow. Finally, you’ll learn how to apply the Lakehouse model to real-world scenarios by building an end-to-end data pipeline and a machine learning workflow while exploring common industry applications like fraud detection, recommendation systems, and customer analytics.

When you’re finished with this course, you’ll have the skills and knowledge of the Databricks Lakehouse platform needed to design and implement modern data solutions for analytics, machine learning, and beyond.