MS10777A: Implementing a Data Warehouse with Microsoft SQL Server 2012
Starting dates and places
Description
MS10777A: Implementing a Data Warehouse with Microsoft SQL Server 2012
Duration
5 days
Course Overview
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 exerci…
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MS10777A: Implementing a Data Warehouse with Microsoft SQL
Server 2012
Duration
5 days
Course Overview
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.
Target Audience
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
Course Objectives
Upon successful completion of this course, delegates will have the
necessary skills 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 Data Flow in an SSIS Package.
• Debug and Troubleshoot SSIS packages.
• Implement an SSIS solution that supports
incremental DW 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 at source.
• Extend SSIS with custom scripts and
components.
• Deploy and Configure SSIS packages.
• Describe how information workers can consume
data from the data warehouse.
Prerequisites
In addition to their professional experience, students who attend
this training should have technical knowledge equivalent to the
following course:
• 10774A: Querying Microsoft SQL Server 2012
Course Contents
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 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
After completing this module, students will be able to:
Describe data warehouse concepts and architecture
considerations.
Module 2: Data Warehouse Hardware Considerations
This 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
After completing this module, students will be able to:
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
After completing this module, students will be able to:
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
After completing this module, students will be able to:
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
After completing this module, students will be able to:
Implement control flow in an SSIS package.
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.
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
After completing this module, students will be able to:
Debug and Troubleshoot SSIS packages.
Module 7: Implementing an Incremental ETL Process
This 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
After completing this module, students will be able to:
Implement an SSIS solution that supports incremental DW loads and
changing data.
Module 8: Incorporating Data from the Cloud in a Data Warehouse
This 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
After completing this module, students will be able to:
Integrate cloud data into a data warehouse ecosystem.
Module 9: Enforcing Data Quality
This 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
After completing this module, students will be able to:
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
After completing this module, students will be able to:
Implement Master Data Services to enforce data integrity at
source.
Module 11: Extending SSIS
This 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
After completing this module, students will be able to:
Extend SSIS with custom scripts and components
Module 12: Deploying and Configuring SSIS Packages
This 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
After completing this module, students will be able to:
Deploy and configure SSIS packages.
Module 13: Consuming Data in a Data Warehouse
This 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
After completing this module, students will be able to:
Describe how information workers can consume data from the data
warehouse.
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