- Adit Modi
This book is a comprehensive framework for developing a modern data analytics architecture. This book views the concept of Data Lakehouse through an architectural lens.
This book will help you understand the need for a new data architecture pattern called Data Lakehouse, the details of the different layers and components of a Data Lakehouse architecture, and the methods required to deploy this architecture in a cloud computing platform and scale it to achieve the macro-patterns of Data Mesh and Hub-spoke.
Who this book is for ?
This book is for people who want to understand how to architect modern analytics. This book targets anyone who wants to become well-versed with modern data architecture patterns to enable large-scale analytics.
It explains concepts in a non-technical and straightforward manner. The book's target audience includes data architects, big data engineers, data strategists and practitioners, data stewards, and cloud computing practitioners.
PART 1: Architectural Patterns for Analytics
This section describes the evolution of data architecture patterns for analytics. It addresses the challenges posed by different architectural patterns and establishes a new paradigm, that is, the data lakehouse.
An overview of the data lakehouse architecture is also provided, which includes coverage of the principles that govern the target architecture, the components that form the data lakehouse architecture, the rationale and need for those components, and the architectural principles adopted to make a data lake scalable and robust.
This section comprises the following chapters: • Chapter 1, Introducing the Evolution of Data Analytics Patterns • Chapter 2, The Data Lakehouse Architecture Overview
PART 2: Data Lakehouse Component Deep Dive
- In this section of the book, we will explore the components of the data lakehouse architecture. The journey will begin with how data is ingested before moving on to how data is stored, served, cataloged, and converted into insights using analytics.
This section comprises the following chapters: • Chapter 3, Ingesting and Processing Data in a Data Lakehouse • Chapter 4, Storing and Serving Data in a Data Lakehouse • Chapter 5, Deriving Insights from a Data Lakehouse • Chapter 6, Applying Data Governance in a Data Lakehouse • Chapter 7, Applying Data Security in a Data Lakehouse
PART 3: Implementing and Governing a Data Lakehouse
This section focuses on the cloud services used to implement and govern a data lakehouse. It will explain the services in Microsoft Azure that you can use with your data lakehouse architecture, the need for a macro architecture pattern for your data lakehouse, and the organizational drivers that dictate the requirements for such a pattern.
Then the section will cover two types of macro architecture patterns, hub-spoke and data mesh, and discuss the concepts of these macro patterns and how the idea of the data lakehouse interlaces with these patterns.
This section comprises the following chapters: • Chapter 8, Implementing a Data Lakehouse on Microsoft Azure • Chapter 9, Scaling the Data Lakehouse Architecture
- I thoroughly enjoyed reading "Data Lakehouse in Action" by Pradeep Menon. Thank you to Shifa Ansari at Packt team for sharing this book and for the opportunity of an Editorial Review of the same. Strongly recommend it if you’re looking to get started with Data and Analytics or build a career as a Data Science Professional or Engineer or Architect.
Personally, I loved 👀👇:
👉 The book starts by exploring the different architecture patterns available for analytics including Data Analytics and Data Lakehouse patterns.
👉 the book does a good job of explaining how different components of data lakehouse work together to store, serve and catalog data, develop ML models, and convert to insights using analytics.
👉 Finally it helps us understand how to effectively implement and govern a data lakehouse using Azure Services and macro architecture patterns.
👉 Link of the Book is here
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