Fakta

Publisher	Springer Nature
Language	English
Book type	Paperback
Utgiven	2020-10-18
Edition	1
Pages	391
ISBN	9781484262122
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Description

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.

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