SQL Server Data Automation Through Frameworks: Building Metadata-Driven Frameworks with T-Sql, Ssis, and Azure Data Factory

內容描述

Learn to automate SQL Server operations using frameworks built from metadata-driven stored procedures and SQL Server Integration Services (SSIS). Bring all the power of Transact-SQL (T-SQL) and Microsoft .NET to bear on your repetitive data, data integration, and ETL processes. Do this for no added cost over what you've already spent on licensing SQL Server. The tools and methods from this book may be applied to on-premises and Azure SQL Server instances. The SSIS framework from this book works in Azure Data Factory (ADF) and provides DevOps personnel the ability to execute child packages outside a project--functionality not natively available in SSIS.
Frameworks not only reduce the time required to deliver enterprise functionality, but can also accelerate troubleshooting and problem resolution. You'll learn in this book how frameworks also improve code quality by using metadata to drive processes. Much of the work performed by data professionals can be classified as "drudge work"--tasks that are repetitive and template-based. The frameworks-based approach shown in this book helps you to avoid that drudgery by turning repetitive tasks into "one and done" operations. Frameworks as described in this book also support enterprise DevOps with built-in logging functionality.
What You Will Learn

Create a stored procedure framework to automate SQL process execution
Base your framework on a working system of stored procedures and execution logging
Create an SSIS framework to reduce the complexity of executing multiple SSIS packages
Deploy stored procedure and SSIS frameworks to Azure Data Factory environments in the cloud

Who This Book Is For
Database administrators and developers who are involved in enterprise data projects built around stored procedures and SQL Server Integration Services (SSIS). Readers should have a background in programming along with a desire to optimize their data efforts by implementing repeatable processes that support enterprise DevOps.

作者介紹

Andy Leonard is Chief Data Engineer at Enterprise Data & Analytics, creator and Data Philosopher at DILM (Data Integration Lifecycle Management) Suite, an Azure Data Factory and SQL Server Integration Services trainer and consultant, and a BimlHero. He is a SQL Server database and data warehouse developer, community mentor, engineer, and farmer. Andy is co-author of SQL Server Integration Services Design Patterns, Data Integration Life Cycle Management with SSIS, and The Biml Book.
Kent Bradshaw is the founder or Tudor Data Solutions, LLC. With over 40 years of IT experience, he is a SQL Server database/ETL developer and database architect with a background is in Medicaid claims, public schools, government, retail, and insurance systems. In 2011, Kent founded Tudor Data Solutions, LLC to pursue new development opportunities which led to his association with Andy Leonard and Enterprise Data & Analytics. In 2017, Kent received the MPP certification for Data Science.