Build custom SQL Server Integration Services (SSIS) tasks using Visual Studio Community Edition and C#. Bring all the power of Microsoft .NET to bear on your data integration and ETL processes, and for no added cost over what you’ve already spent on licensing SQL Server. New in this edition is a demonstration deploying a custom SSIS task to the Azure Data Factory (ADF) Azure-SSIS Integration Runtime (IR). All examples in this new edition are implemented in C#. Custom task developers are shown how to implement custom tasks using the widely accepted and default language for .NET development. Why are custom components necessary? Because even though the SSIS catalog of built-in tasks and components is a marvel of engineering, gaps remain in the available functionality. One such gap is a constraint of the built-in SSIS Execute Package Task, which does not allow SSIS developers to select SSIS packages from other projects in the SSIS Catalog. Examples in this bookshow how to create a custom Execute Catalog Package task that allows SSIS developers to execute tasks from other projects in the SSIS Catalog. Building on the examples and patterns in this book, SSIS developers may create any task to which they aspire, custom tailored to their specific data integration and ETL needs. What You Will Learn Configure and execute Visual Studio in the way that best supports SSIS task development Create a class library as the basis for an SSIS task, and reference the needed SSIS assemblies Properly sign assemblies that you create in order to invoke them from your task Implement source code control via Azure DevOps, or your own favorite tool set Troubleshoot and execute custom tasks as part of your own projects Create deployment projects (MSIs) for distributing code-complete tasks Deploy custom tasks to Azure Data Factory Azure-SSIS IRs in the cloud Create advanced editors for custom task parameters Who This Book Is For For database administrators and developers who are involved in ETL projects built around SQL Server Integration Services (SSIS). Readers do not need a background in software development with C#. Most important is a desire to optimize ETL efforts by creating custom-tailored tasks for execution in SSIS packages, on-premises or in ADF Azure-SSIS IRs.

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). Readersshould have a background in programming along with a desire to optimize their data efforts by implementing repeatable processes that support enterprise DevOps.