Read more
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
About the author
Gaurav Thalpati is an independent consultant with over two decades of experience building data and analytics platforms. He has worked on various data projects and played different roles, including ETL/BI developer, data engineer, data analyst, and data architect. Based in Pune, India, Gaurav is passionate about sharing his knowledge with other data practitioners and guiding them in designing and implementing scalable and cost-effective data platforms.
