With the rise of data volume, velocity, and variety in the modern data and analytics platform on Azure, there is an ever-growing demand for innovative low-cost storage and on-demand compute options that are centered around their decoupled capabilities. This book will enhance your understanding around some of the practical methods of designing and implementing the Data Lakehouse paradigm on Azure by demonstrating the capabilities of Apache Spark and Delta Lake to build cutting-edge modern Lakehouse solutions on Databricks, Synapse Analytics, and Snowflake. You will gain an understanding of these various technologies and how they fit into the modern data and analytics Lakehouse paradigm by supporting the needs of ingestion, processing, storing, serving, reporting, and consumption. You’ll also gain a better understanding of how machine learning, data governance, and continuous integration and deployment play a role in the Lakehouse.

The Data Lakehouse paradigm on Azure, which leverages Apache Spark and Delta Lake heavily, has become a popular choice for big data engineering, ELT (extraction, loading, and transformation), AI/ML, real-time data processing, reporting, and querying use cases. In some scenarios of the Lakehouse paradigm, Spark coupled with MPP is great for big data reporting and BI Analytics platforms that require heavy querying and performance capabilities since MPP is based on the traditional RDBMS and brings with it the best features of SQL Server, such as automated query tuning, data shuffling, ease of analytics platform management, even data distribution based on a primary key, and much more. As the Lakehouse matures, specifically with Delta Lake, it begins to demonstrate its capabilities of supporting many critical features, such as ACID (atomicity, consistency, isolation, and durability)-compliant transactions for batch and streaming jobs, data quality enforcement, and highly optimized performance tuning.

In the upcoming chapters of this book, we will unravel the many complexities of understanding the technologies used within the Lakehouse paradigm along with their capabilities through hands-on, scenario-based exercises. You will learn how to implement advanced performance optimization tools and patterns for Spark performance improvement in the Lakehouse by using partitioning, indexing, and other tuning options. You will also learn about the capabilities of Delta Lake which include schema evolution, change feed, Live Tables, sharing, and clones. Finally, you will gain more knowledge about some of the advanced capabilities within the Lakehouse, such as building and installing custom Python libraries, implementing security and controls, and working with event-driven autoloading data on the Lakehouse platform. The chapters presented within this book are intended to equip you with the right skills and knowledge to design and implement a modern Lakehouse by serving as your Data Lakehouse Toolkit.