SQL Server Data Automation Through Frameworks

Building Metadata-Driven Frameworks with T-SQL, SSIS, and Azure Data Factory
1. Auflage 2020
ISBN: 978-1-4842-6212-2
Verlag: Apress

Buch, Englisch, 391 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 771 g

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

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Bradshaw, Kent

Leonard, Andy

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Microsoft Programmierung
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Datenbankprogrammierung SQL Server/MS SQL

Inhaltsverzeichnis

Part I: Stored Procedure-Based Database Frameworks.- 1. Stored Procedures 101.- 2. Automation with Stored Procedures.- 3. Stored Procedure Orchestrators.- 4. A Stored Procedure-Base Metadata-Driven Framework.- Part II: SSIS Frameworks.- 5. A Simple Custom File-Based SSIS Framework.- 6. Framework Execution Engine.- 7. Framework Logging.- 8. Azure-SSIS Integration Runtime.- 9. Deploy A Simple Custom File-Based Azure-SSIS Framework.- 10. Framework Logging in ADF.- 11. Fault Tolerance in the ADF Framework.

Über Autor(innen)

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.