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Implementing a Data Warehouse with Microsoft SQL Server - 20463 Course Outline

 (5 days)
Version 2014

This course describes how to implement a data warehouse platform to support a BI solution. Students will learn how to create a data warehouse with Microsoft SQL Server 2014, implement ETL with SQL Server Integration Services, and validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services.

This course is designed for customers who are interested in learning SQL Server 2012 or SQL Server 2014. It covers the new features in SQL Server 2014, but also the important capabilities across the SQL Server data platform.

*** NOTE: This class also available in On-Demand, eLearning Format ***
Too busy at work to miss 5 days out of the office to take this class? Consider the MOC On-Demand version of this course. Watch videos developed by Microsoft, take the same hands-on labs, access an online expert 24/7, and study at on your own time, at your own pace. For details on this alternative format, go to
MOC On-Demand 20463: Implementing a Data Warehouse with Microsoft SQL Server 2014

Audience Profile
This course is intended for database professionals who need to create and support a data warehousing solution. Primary responsibilities include:
Implementing a data warehouse.
Developing SSIS packages for data extraction, transformation, and loading.
Enforcing data integrity by using Master Data Services.
Cleansing data by using Data Quality Services.

This course requires that you meet the following prerequisites:
At least 2 years’ experience of working with relational databases, including:
Designing a normalized database.
Creating tables and relationships.
Querying with Transact-SQL.
Some exposure to basic programming constructs (such as looping and branching).
An awareness of key business priorities such as revenue, profitability, and financial accounting is desirable.

At Course Completion
After completing this course, students will be able to:
Describe data warehouse concepts and architecture considerations.
Select an appropriate hardware platform for a data warehouse.
Design and implement a data warehouse.
Implement Data Flow in an SSIS Package.
Implement Control Flow in an SSIS Package.
Debug and Troubleshoot SSIS packages.
Implement an ETL solution that supports incremental data extraction.
Implement an ETL solution that supports incremental data loading.
Implement data cleansing by using Microsoft Data Quality Services.
Implement Master Data Services to enforce data integrity.
Extend SSIS with custom scripts and components.
Deploy and Configure SSIS packages.
Describe how BI solutions can consume data from the data warehouse.

Course Outline

Module 1: Introduction to Data Warehousing
This module provides an introduction to the key components of a data warehousing solution and the high-level considerations you must take into account when you embark on a data warehousing project.
Overview of Data Warehousing
Considerations for a Data Warehouse Solution
Lab : Exploring a Data Warehousing Solution
Exploring Data Sources
Exploring and ETL Process
Exploring a Data Warehouse

Module 2: Planning Data Warehouse Infrastructure
This module discusses considerations for selecting hardware and distributing SQL Server facilities across servers.
Considerations for Data Warehouse Infrastructure
Planning Data Warehouse Hardware
Lab : Planning Data Warehouse Infrastructure
Planning Data Warehouse Hardware

Module 3: Designing and Implementing a Data Warehouse
This module describes the key considerations for the logical design of a data warehouse, and then discusses best practices for its physical implementation.
Data Warehouse Design Overview
Designing Dimension Tables
Designing Fact Tables
Physical Design for a Data Warehouse
Lab : Implementing a Data Warehouse
Implement a Star Schema
Implement a Snowflake Schema
Implement a Time Dimension

Module 4: Creating an ETL Solution with SSIS
This module discusses considerations for implementing an ETL process, and then focuses on Microsoft SQL Server Integration Services (SSIS) as a platform for building ETL solutions.
Introduction to ETL with SSIS
Exploring Data Sources
Implementing Data Flow
Lab : Implementing Data Flow in an SSIS Package
Exploring Data Sources
Transferring Data by Using a Data Flow Task
Using Transformations in a Data Flow

Module 5: Implementing Control Flow in an SSIS Package
This module describes how to implement ETL solutions that combine multiple tasks and workflow logic.
Introduction to Control Flow
Creating Dynamic Packages
Using Containers
Managing Consistency
Lab : Implementing Control Flow in an SSIS Package
Using Tasks and Precedence in a Control Flow
Using Variables and Parameters
Using Containers
Lab : Using Transactions and Checkpoints
Using Transactions
Using Checkpoints

Module 6: Debugging and Troubleshooting SSIS Packages
This module describes how you can debug packages to find the cause of errors that occur during execution. It then discusses the logging functionality built into SSIS that you can use to log events for troubleshooting purposes. Finally, the module describes common approaches for handling errors in control flow and data flow.
Debugging an SSIS Package
Logging SSIS Package Events
Handling Errors in an SSIS Package
Lab : Debugging and Troubleshooting an SSIS Package
Debugging an SSIS Package
Logging SSIS Package Execution
Implementing an Event Handler
Handling Errors in a Data Flow

Module 7: Implementing a Data Extraction Solution
This module describes the techniques you can use to implement an incremental data warehouse refresh process.
Planning Data Extraction
Extracting Modified Data
Lab : Extracting Modified Data
Using a Datetime Column
Using Change Data Capture
Using the CDC Control Task
Using Change Tracking

Module 8: Loading Data into a Data Warehouse
This module describes the techniques you can use to implement data warehouse load process.
Planning Data Loads
Using SSIS for Incremental Loads
Using Transact-SQL Loading Techniques
Lab : Loading a Data Warehouse
Loading Data from CDC Output Tables
Using a Lookup Transformation to Insert or Update Dimension Data
Implementing a Slowly Changing Dimension
Using the MERGE Statement

Module 9: Enforcing Data Quality
This module introduces Microsoft SQL Server Data Quality Services (DQS), and describes how you can use it to cleanse and deduplicate data.
Introduction to Data Quality
Using Data Quality Services to Cleanse Data
Using Data Quality Services to Cleanse Data
Lab : Cleansing Data
Creating a DQS Knowledge Base
Using a DQS Project to Cleanse Data
Using DQS in an SSIS Package

Module 10: Master Data Services
Master Data Services provides a way for organizations to standardize data and improve the quality, consistency, and reliability of the data that guides key business decisions. This module introduces Master Data Services and explains the benefits of using it.
Introduction to Master Data Services
Implementing a Master Data Services Model
Managing Master Data
Creating a Master Data Hub
Lab : Implementing Master Data Services
Creating a Master Data Services Model
Using the Master Data Services Add-in for Excel
Enforcing Business Rules
Loading Data Into a Model
Consuming Master Data Services Data

Module 11: Extending SQL Server Integration Services
This module describes the techniques you can use to extend SSIS. The module is not designed to be a comprehensive guide to developing custom SSIS solutions, but to provide an awareness of the fundamental steps required to use custom components and scripts in an ETL process that is based on SSIS.
Using Scripts in SSIS
Using Custom Components in SSIS
Lab : Using Custom Scripts
Using a Script Task

Module 12: Deploying and Configuring SSIS Packages
In this module, students will learn how to deploy packages and their dependencies to a server, and how to manage and monitor the execution of deployed packages.
Overview of SSIS Deployment
Deploying SSIS Projects
Planning SSIS Package Execution
Lab : Deploying and Configuring SSIS Packages
Creating an SSIS Catalog
Deploying an SSIS Project
Running an SSIS Package in SQL Server Management Studio
Scheduling SSIS Packages with SQL Server Agent

Module 13: Consuming Data in a Data Warehouse
This module introduces business intelligence (BI) solutions and describes how you can use a data warehouse as the basis for enterprise and self-service BI.
Introduction to Business Intelligence
Enterprise Business Intelligence
Self-Service BI and Big Data
Lab : Using a Data Warehouse
Exploring an Enterprise BI Solution
Exploring a Self-Service BI Solution
View outline in Word


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