Practical Lakehouse Architecture

Chapter 7. The Big Picture: Designing and Implementing a Lakehouse Platform

In the preceding chapters, we discussed the individual components of lakehouse architecture and their design considerations. This chapter will discuss how to stitch all these components together to design and implement a modern, scalable, and secure lakehouse platform.

This chapter will help data architects design an end-to-end platform based on lakehouse architecture. It will also guide data engineers in implementing various data management processes and best practices in a lakehouse. Other data personas, like data analysts, scientists, stewards, and platform administrators, can read this chapter to get a detailed understanding of various lakehouse processes and how they may impact their day-to-day work.

We will first discuss the pre-design activities, like requirements gathering and understanding the existing system and its challenges. These activities involve asking the right questions of the right people. Next, I’ll help you to establish the guiding principles that lay the foundation of your lakehouse platform.

I’ll then explain the design considerations for key components like data ingestion, processing, storage, consumption, metadata management, governance, security, and operations. We will discuss the interdependencies between these components and the best approaches for implementing them from a lakehouse perspective.

In the last section of this chapter, I’ll provide you with a step-by-step design ...