Publishing My First Book — A book on data, architecture, analytics, and cloud
As Isabel Allende once quoted, “Writing is a calling, not a choice.”
From writing blogs to now authoring books — I’m beginning my writing career as a published author. My first book on #bigdata #architecture, titled Data Lakehouse Architecture In Action: Architect data the right way to create a robust and scalable data architecture, is available for pre-order now on amazon.com.
This book is a culmination of my passion for data, my experience in data and AI, and my endeavor of simplifying complex topics.
Why did I author this book?
Digital transformation is a reality. All organizations, big or small, must embrace this reality to be relevant in the future. Data is at the core of this transformation and data analytics is the catalyst for this transformation. Therefore, an agile, scalable, and robust architecture is pivotal for shaping data as a strategic asset.
Holistic data architecture is vital for harnessing data’s potential.
However, very few organizations have successfully harnessed their data estate for analytics. Many of them are grappling with obsolete enterprise data warehouse architectural patterns or have jumped into the data lake bandwagon without a comprehensive architectural framework. Also, the trending new term of “Data Lakehouse” focuses on various vendors’ product-centric views rather than an architectural paradigm. I wanted to provide a product-agnostic view of the Data Lakehouse through this book.
This book views the concept of the Data Lakehouse through an architectural lens.
Who is this book for?
I have always strived to make complex topics palatable to everyone. This book follows that trend.
This book targets anyone who wants to become well-versed with modern data architecture patterns to enable large-scale analytics.
The book explains concepts in a non-technical and straightforward manner. Its target audience includes data architects, big data engineers, data strategists and practitioners, data stewards, and cloud computing practitioners.
What does this book contain?
This book is a comprehensive framework for developing a modern data analytics architecture. While authoring this book, I have focused on the architectural constructs of a Data Lakehouse. The book covers different layers and components of architecture. It explores how these different layers synthesize together to form a robust, scalable, and modular architecture deployed on any platform.
Part 1: The context setting
The first part of the book focuses on the evolution of Data architecture and provides an overview of Data Lakehouse architecture. This part has two chapters:
- Chapter 1, Introducing the Evolution of Data Analytics Patterns, provides an overview of the evolution of the data architecture patterns for analytics.
- Chapter 2, The Data Lakehouse Architecture Overview, provides an overview of the various components that form the Data Lakehouse architecture pattern.
Part 2: The deep dive
The second part of the book drills down into details and explains the seven layers of data lakehouse architecture (ingestion, processing, data lake, serving, analytics, governance, and security). This part has three chapters:
- Chapter 3, Ingesting and Processing Data in a Data Lakehouse, deep dives into the methods of ingesting and processing data in a batch and streaming data in a Data Lakehouse.
- Chapter 4, Storing and Serving Data in a Data Lakehouse, discusses the types of datastores of a data lake and various methods to serve data from a Data Lakehouse.
- Chapter 5, Deriving Insights from a Data Lakehouse, discusses how one can perform business intelligence, artificial intelligence, and data exploration.
- Chapter 6, Applying Data Governance in a Data Lakehouse, discusses the ways data can be governed, how to implement and maintain data quality, and how data needs to be cataloged.
- Chapter 7, Applying Data Security in a Data Lakehouse, discusses various components used to secure the Data Lakehouse and ways to provide the proper access to the right users.
Part 3: The implementation and scaling
The third and final part of the book focuses on implementing the architecture in a cloud computing platform (Azure) and scaling the architecture with Data mesh/Hub-Spoke patterns. It has two chapters:
- Chapter 8, Implementing a Data Lakehouse on Microsoft Azure, focuses on implementing the Data Lakehouse in a cloud computing platform like Microsoft Azure.
- Chapter 9, Scaling the Data Lakehouse Architecture, discusses how data lakehouses can be scaled to realize macro-architecture patterns of Data Mesh and Hub-Spoke.
I know the book is not perfect. However, it attempts to create a structure and shape an idea. I’m eagerly looking forward to the support and feedback from the readers and continuing this incredible journey ahead.