Practical Lakehouse Architecture - Gaurav Thalpati

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Résumé :
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....

Biographie:
Gaurav Thalpati is an independent consultant specializing in cloud data architecture and data management, previously serving as a data architect at Blazeclan Technologies. He has over 18 years of experience working on data projects in various capacities and has held a variety of roles, including ETL developer, data analyst, and data engineer. Currently, Gaurav uses his expertise in his consultation work to guide and mentor data teams to design and implement scalable and cost-effective data platforms. He is also an avid blogger and enjoys sharing his experiences from working on data projects across multiple industries with fellow data professionals.

Sommaire:

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