Implementing a Data Warehouse with Microsoft SQL Server

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
 

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 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.

 

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 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
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
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
• 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.
 

Training Schedule

Implementing a Data Warehouse with Microsoft SQL Server
2019 - 2020
Training schedule not found.
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