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Data Lakehouse in Action

Data Lakehouse in Action

By : Pradeep Menon
4.1 (9)
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Data Lakehouse in Action

Data Lakehouse in Action

4.1 (9)
By: Pradeep Menon

Overview of this book

The Data Lakehouse architecture is a new paradigm that enables large-scale analytics. This book will guide you in developing data architecture in the right way to ensure your organization's success. The first part of the book discusses the different data architectural patterns used in the past and the need for a new architectural paradigm, as well as the drivers that have caused this change. It covers the principles that govern the target architecture, the components that form the Data Lakehouse architecture, and the rationale and need for those components. The second part deep dives into the different layers of Data Lakehouse. It covers various scenarios and components for data ingestion, storage, data processing, data serving, analytics, governance, and data security. The book's third part focuses on the practical implementation of the Data Lakehouse architecture in a cloud computing platform. It focuses on various ways to combine the Data Lakehouse pattern to realize macro-patterns, such as Data Mesh and Data Hub-Spoke, based on the organization's needs and maturity level. The frameworks introduced will be practical and organizations can readily benefit from their application. By the end of this book, you'll clearly understand how to implement the Data Lakehouse architecture pattern in a scalable, agile, and cost-effective manner.
Table of Contents (14 chapters)
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1
PART 1: Architectural Patterns for Analytics
4
PART 2: Data Lakehouse Component Deep Dive
10
PART 3: Implementing and Governing a Data Lakehouse

Chapter 4: Storing and Serving Data in a Data Lakehouse

The journey so far has covered a lot of ground, storing data in a data lake is the new architecture paradigm for data architecture. The first chapter covered trends in big data and discussed the need for a new paradigm. The second chapter provided an overview of the data lakehouse architecture and discussed the seven layers of a data lakehouse. The third chapter focused on the methods in which data can be ingested and processed in a data lakehouse. In this chapter, we will focus on storing the data in the data lake and the data serving layers of the data lakehouse architecture.

Data storage is critical from both a storage and performance perspective. This chapter will begin by providing a view of how data is stored in the data lake layer. Next, we will discuss the different data stores within a data lake, along with their needs and benefits. We will then explore the standard data formats used for storing data in a data lake...

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Data Lakehouse in Action
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