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Implementing a Data Warehouse with Microsoft SQL Server 2012 - 10777 Course Outline

 (5 days)
Version 2012

Important Update

Please note that course 10777 has been retired and has been replaced by course 20463.

20463: Implementing a Data Warehouse with Microsoft SQL Server is a new, upgraded course that parallels the 10777, and now includes SQL 2014.

This new course has been 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.

To register, please see:

20463: Implementing a Data Warehouse with Microsoft SQL Server

Data warehousing is a solution organizations use to centralize business data for reporting and analysis. This five-day instructor-led course focuses on teaching individuals how to create a data warehouse with SQL Server 2012, 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 helps people prepare for exam 70-463.

Audience Profile
This course is intended for database professionals who need to fulfill a Business Intelligence Developer role. They will need to focus on hands-on work creating BI solutions including Data Warehouse implementation, ETL, and data cleansing. Primary responsibilities include:
Implementing a data warehouse.
Developing SQL Server Integration Services (SSIS) packages for data extraction, transformation, and loading (ETL).
Enforcing data integrity by using Master Data Services.
Cleansing data by using Data Quality Services.

Before attending this course, student should have:
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 SSIS solution that supports incremental data warehouse loads and changing data.
Integrate cloud data into a data warehouse ecosystem infrastructure.
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 information workers 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 starting a data warehousing project.
Overview of Data Warehousing
Considerations for a Data Warehouse Solution
Lab : Exploring a Data Warehousing Solution
Exploring data sources
Exploring an ETL solution
Exploring a data warehouse

Module 2: Data Warehouse Hardware
This module describes the characteristics of typical data warehouse workloads, and explains how you can use reference architectures and data warehouse appliances to ensure you build the system that is right for your organization.
Considerations for Building a Data Warehouse
Data Warehouse Reference Architectures and Appliances

Module 3: Designing and Implementing a Data Warehouse
In this module, you will learn how to implement the logical and physical architecture of a data warehouse based on industry-proven design principles.
Logical Design for a Data Warehouse
Physical Design for a Data Warehouse
Lab : Implementing a Data Warehouse Schema
Implementing a Star Schema
Implementing a Snowflake Schema
Implementing a Time Dimension Table

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

Module 5: Implementing Control Flow in an SSIS Package
Control flow in SQL Server Integration Services packages enables you to implement complex ETL solutions that combine multiple tasks and workflow logic. This module covers how to implement control flow, and design robust ETL processes for a data warehousing solution that coordinate data flow operations with other automated tasks.
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 SQL Server Integration Services (SSIS) packages to find the cause of errors that occur during execution. Then module then covers the logging functionality built into SSIS 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 an Incremental ETL Process
This module describes the techniques you can use to implement an incremental data warehouse refresh process.
Introduction to Incremental ETL
Extracting Modified Data
Loading Modified Data
Lab : Extracting Modified Data
Using a DateTime Column to Incrementally Extract Data
Using a Change Data Capture
Using Change Tracking
Lab : Loading Incremental Changes
Using a Lookup Transformation to Insert Dimension Data
Using a Lookup Transformation to Insert or Update Dimension Data
Implementing a Slowly Changing Dimension
Using a MERGE Statement to Load Fact Data

Module 8: Incorporating Data from the Cloud into a Data Warehouse
In this module, you will learn about how you can use cloud computing in your data warehouse infrastructure and learn about the tools and services available from the Microsoft Azure Marketplace.
Overview of Cloud Data Sources
SQL Server Database
The Windows Azure Marketplace
Lab : Using Cloud Data in a Data Warehouse Solution
Creating a SQL Azure Database
Extracting Data from a SQL Azure Database
Obtaining Data from the Windows Azure Marketplace

Module 9: Enforcing Data Quality
Ensuring the high quality of data is essential if the results of data analysis are to be trusted. This module explains how to use the SQL Server 2012 Data Quality Services (DQS) to provide a computer assisted process for cleansing data values and identifying and removing duplicate data entities.
Introduction to Data Quality
Using Data Quality Services to Cleanse Data
Using Data Quality Services to Match Data
Lab : Cleansing Data
Creating a DQS Knowledge Base
Using a DQS Project to Cleanse Data
Using DQS in an SSIS Package
Lab : Deduplicating Data
Creating a Matching Policy
Using a DQS Project to Match Data

Module 10: Using Master Data Services
This module introduces Master Data Services and explains the benefits of using it in a data warehousing context. The module also describes the key configuration options for Master Data Services, and explains how to import and export data. Finally, the module explains how to apply rules that help to preserve data integrity, and introduces the new Master Data Services Add-in for Excel.
Introduction to Master Data Services
Implementing a Master Data Services Model
Using the Master Data Services Add-in for Excel
Lab : Implementing Master Data Services
Creating a Basic Model
Editing a Model by Using the Master Data Services Add-in for Excel
Loading Data into a Model
Enforcing Business Rules
Consuming Master Data Services Data

Module 11: Extending SQL Server Integration Services
This module describes the techniques you can use to extend SQL Server Integration Services (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 Custom Components in SSIS
Using Scripts in SSIS
Lab : Using Custom Components and Scripts
Using a Custom Component
Using a Script Task

Module 12: Deploying and Configuring SSIS Packages
SQL Server Integration Services provides tools that make it easy to deploy packages to another computer. The deployment tools also manage any dependencies, such as configurations and files that the package needs. In this module, you will learn how to use these tools to install packages and their dependencies on a target computer.
Overview of SSIS Deployment
Deploying SSIS Projects
Planning SSIS Package Execution
Lab : Deploying and Configuring SSIS Packages
Create a SSIS Catalog
Deploy an SSIS Project
Create Environments for an SSIS Solution
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), describes the components of SQL Server that you can use to create a BI solution, and the client tools that users can use to create reports and analyze data.
Introduction to Business Intelligence
Introduction to Reporting
Introduction to Data Analysis
Lab : Using Business Intelligence Tools
Exploring a Reporting Services Report
Exploring a PowerPivot Workbook
Exploring a Power View Report

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