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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 5: Deriving Insights from a Data Lakehouse

A lot of ground has been covered so far. The previous chapters covered the methods of ingesting, processing, storing, and serving data in a data lakehouse. Transforming the underlying data into insights is the core aim of any data analytics platform, so this chapter will focus on how to do this. We will also explore the different kinds of data analytics that can be employed in this process.

First, we'll discuss some of the business requirements relating to data analytics. Then, we'll explore different kinds of data analytics and how different stakeholders can use them. After that, we will dive into how these capabilities are enabled using the three components of the data analytics layer: the analytics sandbox, the business intelligence service, and the artificial intelligence service. We will cover different types of descriptive and advanced data analytics. We will also focus on the methods of enabling these analytics...

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Data Lakehouse in Action
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