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


Advanced analytics, including statistics, data mining, and machine learning, has become very popular in recent years. You can use SSIS to prepare the data you need for further analysis. Often, you need to prepare a sample of your data. The sample has to be random. For predictive algorithms, you typically split the data into a training set, used to train multiple models, and a test set, used to perform predictions on it, and see which model gives you the best results. You can use the row sampling and the percentage sampling transformations to create random samples.

In the SQL Server suite, you can use SQL Server Analysis Services (SSAS), installed in multidimensional and data mining mode, to create data mining models. In addition, from SQL Server 2016, you can also use the R language to do nearly any kind of advanced analysis you want. You will learn in this chapter how you can use both SSAS and R models in the SSIS data flow.

You will use the data mining query transformation for...