Book Image

SQL Server 2017 Integration Services Cookbook

By : Christian Cote, Dejan Sarka, David Peter Hansen, Matija Lah, Samuel Lester, Christo Olivier
Book Image

SQL Server 2017 Integration Services Cookbook

By: Christian Cote, Dejan Sarka, David Peter Hansen, Matija Lah, Samuel Lester, Christo 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 (18 chapters)
Title Page
About the Authors
About the Reviewers
Customer Feedback

Preparing a Naive Bayes SSAS data mining model

In this recipe, you will determine the factors that influence buying bikes. You will use the Naive Bayes algorithm with the training set you prepared in the previous recipe.

Getting ready

For this recipe, you need to have SSAS installed in multidimensional and data mining mode. You also need to finish the first recipe in this chapter.

How to do it...

  1. In SSDT, add a new analysis services multidimensional and data mining project to the Chpater08 solution. Name it Ch08NaiveBayes.
  2. Create a new data source using the AdventureWorksDW2014 database. Use the Native OLE DB\SQL Server Native Client 11.0 provider. Use a Windows user that has permission to read the data. Use the default name for the data source.
  3. Create a new data source view based on the data source created in step 2. Select the TMTrainingSet and TMTestSet tables created in the first recipe of this chapter for the training and test sets. Use the default name for the data source view.
  4. Set the CustomerKey...