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Mar052012

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

Course 10777 – Implementing a Data Warehouse with Microsoft SQL Server 2012

About this Course

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.

Audience Profile

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

In addition to their professional experience, students who attend this training should have technical knowledge equivalent to the following course:

  • ·         10774A: Writing Queries with Microsoft SQL Server Transact-SQL

 

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 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 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

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 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

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

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 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

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

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

  • ·        
  • ·         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.

 

In addition to their professional experience, students who attend this training should have technical knowledge equivalent to the following course:

  • ·         10774A: Writing Queries with Microsoft SQL Server Transact-SQL

 

 

 

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