10777 - Implementing a Data Warehouse with Microsoft SQL Server 2012

This 5-day instructor-led course describes how to implement a BI platform to support information worker analytics. Students will learn 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 the exam 70-463.
 
The Beta version of this course (10777AB) utilizes pre-release software in the virtual machine for the labs. Microsoft SQL Server 2012 Release Candidate 0 (RC0) is used in this course. Some of the exercises in this course are SQL Azure enabled.

At Course Completion

Course OutlineModule 1: Introduction to Data WarehousingThis module provides an introduction to the key components of a data warehousing solution and the high-level considerations you must take into account when embarking on a data warehousing project.Lessons

    Describe data warehouse concepts and architecture considerations
    Considerations for a Data Warehouse Solution

Lab : Exploring a Data Warehousing Solution

    Exploring Data Sources
    Exploring an ETL Process
    Exploring a Data Warehouse

Describe data warehouse concepts and architecture considerations.
Module 2: Data Warehouse Hardware ConsiderationsThis module describes the considerations for selecting the appropriate hardware platform for your data warehouse solution.Lessons

    The Challenges of Building a Data Warehouse
    Data Warehouse Reference Architectures
    Data Warehouse Appliances

Lab : No lab
Select an appropriate hardware platform for a data warehouse.
Module 3: Designing and Implementing a Data Warehouse
This module describes how to implement the logical and physical architecture of a data warehouse based on industry proven design principles.
Lessons

    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
    Implement a Time Dimension Table

Design and implement a schema for a data warehouse.
Module 4: Design and implement a schema for a data warehouse
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.
Lessons

    Introduction to ETL with SSIS
    Exploring Source Data
    Implementing Data Flow

Lab : Implementing Data Flow in an SSIS Package

    Exploring Source Data
    Transfer Data with a Data Flow Task
    Using Transformations in a Data Flow

Implement Data Flow in an SSIS Package
Module 5: Implementing Control Flow in an SSIS Package
This module describes how to implement control flow which allows users to design robust ETL processes for a data warehousing solution that coordinate data flow operations with other automated tasks.
Lessons

    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

Implement control flow in an SSIS package.
Module 6: Debugging and Troubleshooting SSIS PackagesThis 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.Lessons

    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

Debug and Troubleshoot SSIS packages.
Module 7: Implementing an Incremental ETL ProcessThis module describes the techniques you can use to implement an incremental data warehouse refresh process.Lessons

    Introduction to Incremental ETL
    Extracting Modified Data
    Loading Modified Data

Lab : Extracting Modified Data

    Using a DateTime Column to Incrementally Extract Data
    Using a DateTime Column to Incrementally Extract Data
    Using Change Tracking

Lab : Loading Incremental Changes

    Using a Lookup task to insert dimension data
    Using a Lookup task to insert or update dimension data
    Implementing a Slowly Changing Dimension
    Using a MERGE statement to load fact data

Implement an SSIS solution that supports incremental DW loads and changing data.
Module 8: Incorporating Data from the Cloud in a Data WarehouseThis modules describes how integrate cloud data into a data warehouse ecosystem.Lessons

    Overview of Cloud Data Sources
    SQL Server Azure
    Azure Data Market

Lab : Using Cloud data in a Data Warehouse Solution

    Extracting data from SQL Azure
    Acquiring Data from the Azure Data Market

Integrate cloud data into a data warehouse ecosystem.
Module 9: Enforcing Data QualityThis modules describes how to use Data Quality Services (DQS) for cleansing and deduplicating your data.Lessons

    Introduction to Data Cleansing
    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
    Use DQS in an SSIS Package

Lab : De-Duplicating Data

    Creating a Matching Policy
    Using a DQS Project to Match Data

Implement data cleansing by using Microsoft Data Quality Services.
Module 10: Using Master Data Services
This module introduces Master Data Services and explains the benefits of using it in a business intelligence (BI) context. It also describes the key configuration options, explains how to import and export data and apply rules that help to preserve data integrity, and introduces the new Master Data Services Add-in for Excel.
Lessons

    Master Data Services Concepts
    Implementing a Master Data Services Model
    Using the Master Data Services Excel Add-in

Lab : Implementing Master Data Services

    Creating a Basic MDS Model
    Editing an MDS Model With Excel
    Loading Data into MDS
    Enforcing Business Rules
    Consuming Master Data Services Data

Implement Master Data Services to enforce data integrity at source.
Module 11: Extending SSISThis module describes how to extend SSIS by using custom scripts and components.Lessons

    Using Custom Components in SSIS
    Using Scripting in SSIS

Lab : Using Scripts and Custom Components

    Using a Custom Component
    Using the Script Task

Extend SSIS with custom scripts and components
Module 12: Deploying and Configuring SSIS PackagesThis modules describes how to deploy and configure SSIS packages.Lessons

    Overview of Deployment
    Deploying SSIS Projects
    Planning SSIS Package Execution

Lab : Deploying and Configuring SSIS Packages

    Create an 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

Deploy and configure SSIS packages.
Module 13: Consuming Data in a Data WarehouseThis module describes how information workers can consume data from the data warehouse.Lessons

    Using Excel to Analyze Data in a data Warehouse.
    An Introduction to PowerPivot
    An Introduction to Crescent

Lab : Using a Data Warehouse

    Use PowerPivot to Query the Data Warehouse
    Visualizing Data by Using Crescent

Describe how information workers can consume data from the data warehouse.

The primary audience for this course are database professionals who need to fulfil 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 will  include:

    Implementing as data warehouse
    Developing SSIS packages for data extraction and loading/transfer/transformation
    Enforcing data integrity using Master Data Services
    Cleansing data using Data Quality Services