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SQL Server 2017 Integration Services Cookbook

SQL Server 2017 Integration Services Cookbook

By : Cote, Dejan Sarka, Lah, David Peter Hansen, Samuel Lester, Olivier
2.6 (12)
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SQL Server 2017 Integration Services Cookbook

SQL Server 2017 Integration Services Cookbook

2.6 (12)
By: Cote, Dejan Sarka, Lah, David Peter Hansen, Samuel Lester, Olivier

Overview of this book

SQL Server Integration Services is a tool that facilitates data extraction, consolidation, and loading options (ETL), SQL Server coding enhancements, data warehousing, and customizations. With the help of the recipes in this book, you’ll gain complete hands-on experience of SSIS 2017 as well as the 2016 new features, design and development improvements including SCD, Tuning, and Customizations. At the start, you’ll learn to install and set up SSIS as well other SQL Server resources to make optimal use of this Business Intelligence tools. We’ll begin by taking you through the new features in SSIS 2016/2017 and implementing the necessary features to get a modern scalable ETL solution that fits the modern data warehouse. Through the course of chapters, you will learn how to design and build SSIS data warehouses packages using SQL Server Data Tools. Additionally, you’ll learn to develop SSIS packages designed to maintain a data warehouse using the Data Flow and other control flow tasks. You’ll also be demonstrated many recipes on cleansing data and how to get the end result after applying different transformations. Some real-world scenarios that you might face are also covered and how to handle various issues that you might face when designing your packages. At the end of this book, you’ll get to know all the key concepts to perform data integration and transformation. You’ll have explored on-premises Big Data integration processes to create a classic data warehouse, and will know how to extend the toolbox with custom tasks and transforms.
Table of Contents (12 chapters)
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Querying the SSAS data mining model with the data mining query transformation


In this recipe, you are going to use the data mining query transformation. Based on the Naive Bayes model built in the previous recipe, you will use a (Data Mining Extensions (DMX) prediction query to get predictions from the SSAS mining model for the test dataset you created in the first recipe of this chapter.

Getting ready

In order to test this recipe, you need to have SSAS installed in multidimensional and data mining mode. In addition, you need to finish the first and third recipes of this chapter.

Note

For your convenience, the SSIS and SSAS projects needed here are provided in the Chapter08 solution.

How to do it...

  1. Add a new package to the Chapter08 project. Rename it DataMining.dtsx.
  2. In the control flow of the package, add a new data flow task by dragging it from the SSIS toolbox to the control flow work area.
  3. Click the Data Flow tab to open the Data Flow Designer.
  4. Create a new OLE DB source. Name it TMTestSet...
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