As companies began collecting vast amounts of data from various sources, data architects envisioned a unified system for diverse usage scenarios, including analytics and machine learning (ML) workloads. Historically, solutions like databases, data warehouses, and data lakes have addressed this need, each with distinct benefits and limitations. The advent of cloud technology has prompted data teams to rethink their approaches, leading to the modern cloud data platform architecture known as the lakehouse.

The lakehouse architecture simplifies enterprise data infrastructure and accelerates innovation, merging the best aspects of data lakes and data warehouses. This supports traditional data warehouse use cases and meets new demands posed by incorporating AI, such as computer vision, speech models, and text mining. Consequently, it is not just about system capabilities but also about the architectural approach.

This book helps you understand how the lakehouse makes your data management efforts more effective and efficient in your company. It focuses on describing current data management limitations, explaining the lakehouse concept and its values, and providing guidance on building a modern cloud data platform with Databricks.