Data warehouses and data lakes each evolved to meet a set of specific technology and business needs and values. As organizations often need both, there has been increasing demand for convergence of both technologies. Thus, the lakehouse was born. A lakehouse couples the cost benefits and versatility of data lakes with the data structure and high-performance data management capabilities of data warehouses into a single unified data store that can be consistently and efficiently accessed, governed, analyzed and consumed by AI applications. Lakehouses are designed to help organizations get more from their existing investment in data warehouses and data lakes. It supports the existence of both through access to and management of a larger variety of combined data for increased flexibility, enhancing business intelligence and AI initiatives by revealing deeper insights into an organization's data estates. Lakehouses can provide users with the following abilities: - Understand and anticipate customer behaviors with more complete, governed insights. - Spot patterns and trends to reduce waste and overhead through more diverse analytic and AI techniques. - Promote auditability and transparency with metadata-powered, native data access in a governed data lake. - Speed time to value with self-service data exploration and discovery for users. - Increase collaboration and reduce the time and cost of managing disparate systems and tools in an integrated environment. - Turn open-source and ecosystem investments into innovation opportunities with enterprise-ready, secure data lakes. - Significant cost savings Lakehouses have the ability to: - Reuse the data lake for 360-degree customer and operational intelligence, governance, and risk and compliance reporting - Ingest and integrate with transactional, operational, and analytical data to promote a complete insight - Extend information architectures to provide the right data at the right time on a common foundation for staging, storage, and access Build and maintain a data foundation that powers data cataloging, curation, exploration, and discovery needs Take a hybrid approach to access any data from any locations spanning years of records to real-time data - Integrate and expand analytics across multiple data repositories to drive innovation and optimization at scale. This book is intended for technical communities, such as developers, data scientists, and C-level IT executives, as well as business communities, such as business managers requiring self-service analytics / AI, and C-level business executives.