This concise yet comprehensive guide explains how to adopt a data lakehouse architecture to implement modern data platforms. It reviews the design considerations, challenges, and best practices for implementing a lakehouse and provides key insights into the ways that using a lakehouse can impact your data platform, from managing structured and unstructured data and supporting BI and AI/ML use cases to enabling more rigorous data governance and security measures.
Practical Lakehouse Architecture shows you how to:
Understand key lakehouse concepts and features like transaction support, time travel, and schema evolution
Understand the differences between traditional and lakehouse data architectures
Differentiate between various file formats and table formats
Design lakehouse architecture layers for storage, compute, metadata management, and data consumption
Implement data governance and data security within the platform
Evaluate technologies and decide on the best technology stack to implement the lakehouse for your use case
Make critical design decisions and address practical challenges to build a future-ready data platform
Start your lakehouse implementation journey and migrate data from existing systems to the lakehouse
