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

Transferring data between Hadoop and Azure

Now that we have some data created by Hadoop Hive on-premises, we're going to transfer this data to a cloud storage on Azure. Then, we'll do several transformations to it using Hadoop Pig Latin. Once done, we'll transfer the data to an on-premises table in the staging schema of our AdventureWorksLTDW2016 database.

In this recipe, we're going to copy the data processed by the local Hortonworks cluster to an Azure Blob storage. Once the data is copied over, we can transform it using Azure compute resources, as we'll see in the following recipes.

Getting ready

This recipe assumes that you have created a storage space in Azure as described in the previous recipe.

How to do it...

  1. Open the ETL.Staging SSIS project and add a new package to it. Rename it StgAggregateSalesFromCloud.dtsx.
  2. Add a Hadoop connection manager called cmgr_Hadoop_Sandbox like we did in the previous recipe.
  3. Add another connection manager, which will connect to the Azure storage like the...