Book Image

Mastering SQL Server 2017

By : Miloš Radivojević, Dejan Sarka, William Durkin, Christian Cote, Matija Lah
Book Image

Mastering SQL Server 2017

By: Miloš Radivojević, Dejan Sarka, William Durkin, Christian Cote, Matija Lah

Overview of this book

Microsoft SQL Server 2017 uses the power of R and Python for machine learning and containerization-based deployment on Windows and Linux. By learning how to use the features of SQL Server 2017 effectively, you can build scalable apps and easily perform data integration and transformation. You’ll start by brushing up on the features of SQL Server 2017. This Learning Path will then demonstrate how you can use Query Store, columnstore indexes, and In-Memory OLTP in your apps. You'll also learn to integrate Python code in SQL Server and graph database implementations for development and testing. Next, you'll get up to speed with designing and building SQL Server Integration Services (SSIS) data warehouse packages using SQL server data tools. Toward the concluding chapters, you’ll discover how to develop SSIS packages designed to maintain a data warehouse using the data flow and other control flow tasks. By the end of this Learning Path, you'll be equipped with the skills you need to design efficient, high-performance database applications with confidence. This Learning Path includes content from the following Packt books: SQL Server 2017 Developer's Guide by Miloš Radivojevi?, Dejan Sarka, et. al SQL Server 2017 Integration Services Cookbook by Christian Cote, Dejan Sarka, et. al
Table of Contents (20 chapters)
Title Page
Free Chapter
1
Introduction to SQL Server 2017

Framework calls in EP_Staging.dtsx

This section of the book introduces an ETL framework. The purposes of such a framework are the following:

  • Store execution statistics outside the SSIS catalog: although the Microsoft SSIS team always enhances catalog performance, keeping all execution data in the catalog will degrade catalog performance.
  • Align the data in such a way to be able to do some analytics on the execution data. In that case, think of the framework as an SSIS package execution data warehouse.

The following diagram is the ER diagram of the framework tables. These tables are stored in the SystemLog schema:

Here's the table list and their purposes:

  • LoadApplications: This table contains one value: SSISCookBook sample solution. The purpose of this table, coupled with the Loads table that we'll describe next, is to group many SSIS executions together into a common...