Build a modern data warehouse on Microsoft's Azure Platform that is flexible, adaptable, and fast--fast to snap together, reconfigure, and fast at delivering results to drive sound decision-making in your business. Gone are the days when data warehousing projects were lumbering dinosaur-style projects that took forever, drained budgets, and produced business intelligence (BI) just in time to tell you what to do ten years ago. This book will show you how to assemble a data warehouse solution like a jigsaw puzzle by connecting specific Azure technologies that address your needs and bring value to your business. You will see how to implement a range of architectural patterns using batches, events, and streams for both data lake technology and SQL databases. You will discover how to manage metadata and automation to accelerate the development of your warehouse while establishing resilience at every level. And you will know how to feed downstream analytic solutions such as Power BI and Azure Analysis Services to empower data-driven decision-making that drives your business toward a pattern of success. This book teaches you how to employ the Azure platform in a strategy to dramatically improve the implementation speed and flexibility of data warehousing systems. You will know how to make correct decisions in design, architecture, and infrastructure, such as choosing which type of SQL engine (from at least three options) best meets your organisation's needs. You also will learn about ETL/ELT structure and the vast number of accelerators and patterns that can be used to aid implementation and ensure resilience. Data warehouse developers and architects will find this book a tremendous resource for moving their skills into the future through cloud-based deployments. You will: Choose the appropriate Azure SQL engine for implementing a given data warehouse, Develop innovative, reusable ETL/ELT processes that are resilient and easily maintained. Automate mundane development tasks through tools such as PowerShell, Ensure consistency of data by creating and enforcing data contracts, Explore streaming and event-driven architectures for data ingestion Create advanced staging layers using Azure Data Lake Gen 2 to feed your data warehouse