What this book covers
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.
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 of serving data from a Data Lakehouse.
Chapter 5, Deriving Insights from a Data Lakehouse, discusses the ways in which business intelligence, artificial intelligence, and data exploration can be carried out.
Chapter 6, Applying Data Governance in a Data Lakehouse, discusses how 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 proper access to the right users.
Chapter 8, Implementing a Data Lakehouse on Microsoft Azure, focuses on implementing a Data Lakehouse on the Microsoft Azure cloud computing platform.
Chapter 9, Scaling the Data Lakehouse Architecture, discusses how Data Lakehouses can be scaled to realize the macro-architecture patterns of Data Mesh and Hub-spoke.
