Preface
Digital transformation is a reality. All organizations, big or small, have to embrace this reality to be relevant in the future. Data is at the core of this, and data analytics is the catalyst for this transformation. Therefore, an agile, scalable, and robust data architecture for analytics is pivotal for forging data as a strategic asset.
However, very few organizations can successfully harness their data estate for analytics. Many of them grapple with obsolete enterprise data warehouse architectural patterns or have jumped onto the data lake bandwagon without a proper architectural framework. Also, the new trending term "Data Lakehouse" focuses on various vendors' product-centric views rather than an architectural paradigm. This book views the concept of Data Lakehouse through an architectural lens.
This book is a comprehensive framework for developing a modern data analytics architecture. While writing this book, I have focused on architectural constructs of a Data Lakehouse. The book covers different layers and components of architecture. It explores how these different layers interoperate to form a robust, scalable, and modular architecture that can be deployed on any platform.
By the end of this book, you will 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.
