Implementing a Data Warehouse with Microsoft SQL Server (MS-20463)

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This course describes how to implement a data warehouse platform to support a BI solution.

Virtual Classroom
Learning Style
Intermediate
Difficulty
5 Days
Course Duration
5 Days
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This course describes how to implement a data warehouse platform to support a BI solution.

Course Information

About this course:

This course from Microsoft provides learners with the knowledge and skills required to design, implement, and load a relational data warehouse. In this course, you’ll learn how to analyze business requirements, design dimension and fact tables, index and partition the data for performance, and create ETL solutions for ongoing loading of the warehouse.

This five-day course describes how to implement a data warehouse platform to support a BI solution. Students will learn how to create a data warehouse with Microsoft® SQL Server® 2014, implement ETL with SQL Server Integration Services, and validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services.

Note: This course is 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.

This course also prepares the students for the Microsoft 70-463: Implementing a Data Warehouse with Microsoft SQL Server 2012/2014 certification exam.

The average salary for a Microsoft Data Warehousing Professional is $110,000 per year.

Course Objectives:

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 ETL solution that supports incremental data extraction.
Implement an ETL solution that supports incremental data loading.
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 BI solutions can consume data from the data warehouse.

Audience:

This course is intended for 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 include:

Implementing a data warehouse.
Developing SSIS packages for data extraction, transformation, and loading.
Enforcing data integrity by using Master Data Services.
Cleansing data by using Data Quality Services.

Prerequisites:

This course requires that you meet the following prerequisites:

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.

Suggested prerequisites courses:

Course Outline

Virtual Instructed-Led 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 you embark on a data warehousing project.

Lessons

Overview of Data Warehousing
Considerations for a Data Warehouse Solution

Lab:

Exploring a Data Warehousing Solution
Exploring Data Sources
Exploring and ETL Process
Exploring a Data Warehouse

After completing this module, you will be able to:

Describe the key elements of a data warehousing solution
Describe the key considerations for a data warehousing project

Module 2: Planning Data Warehouse Infrastructure

This module discusses considerations for selecting hardware and distributing SQL Server facilities across servers.

Lessons

Considerations for Data Warehouse Infrastructure
Planning Data Warehouse
Hardware

Lab:

Planning Data Warehouse Infrastructure
Planning Data Warehouse Hardware

After completing this module, you will be able to:

Describe key considerations for BI infrastructure.
Plan data warehouse infrastructure.

Module 3: Designing and Implementing a Data Warehouse

This module describes the key considerations for the logical design of a data warehouse, and then discusses best practices for its physical implementation.

Lessons

Data Warehouse Design
Overview Designing Dimension Tables
Designing Fact Tables
Physical Design for a Data Warehouse

Lab:

Implementing a Data Warehouse
Implement a Star Schema
Implement a Snowflake Schema
Implement a Time Dimension

After completing this module, you will be able to:

Describe a process for designing a dimensional model for a data warehouse
Design dimension tables for a data warehouse
Design fact tables for a data warehouse
Design and implement effective physical data structures for a data warehouse

Module 4: Creating an ETL Solution with SSIS

This module discusses considerations for implementing an ETL process, and then focuses on Microsoft SQL Server Integration Services (SSIS) as a platform for building ETL solutions.

Lessons

Introduction to ETL with SSIS
Exploring Data Sources
Implementing Data Flow

Lab:

Implementing Data Flow in an SSIS Package
Exploring Data Sources
Transferring Data by Using a Data Flow Task
Using Transformations in a Data Flow

After completing this module, you will be able to:

Describe the key features of SSIS.
Explore source data for an ETL solution.
Implement a data flow by using SSIS

Module 5: Implementing Control Flow in an SSIS Package

This module describes how to implement ETL solutions that combine multiple tasks and workflow logic.

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, you will be able to:

Implement control flow with tasks and precedence constraints
Create dynamic packages that include variables and parameters
Use containers in a package control flow
Enforce consistency with transactions and checkpoints

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, you will be able to:

Debug an SSIS package
Implement logging for an SSIS package
Handle errors in an SSIS package

Module 7: Implementing a Data Extraction Solution

This module describes the techniques you can use to implement an incremental data warehouse refresh process.

Lessons

Planning Data Extraction
Extracting Modified Data

Lab:

Extracting Modified Data
Using a Date time Column
Using Change Data Capture
Using the CDC Control Task
Using Change Tracking

After completing this module, you will be able to:

Plan data extraction
Extract modified data

Module 8: Loading Data into a Data Warehouse

This module describes the techniques you can use to implement data warehouse load process.

Lessons

Planning Data Loads
Using SSIS for Incremental Loads
Using Transact-SQL Loading Techniques

Lab:

Loading a Data Warehouse
Loading Data from CDC Output Tables
Using a Lookup Transformation to Insert or Update Dimension Data
Implementing a Slowly Changing Dimension
Using the MERGE Statement

After completing this module, you will be able to:

Describe the considerations for planning data loads
Use SQL Server Integration Services (SSIS) to load new and modified data into a data warehouse
Use Transact-SQL techniques to load data into a data warehouse

Module 9: Enforcing Data Quality

This module introduces Microsoft SQL Server Data Quality Services (DQS), and describes how you can use it to cleanse and duplicate data.

Lessons

Introduction to Data Quality
Using Data Quality Services to Cleanse Data
Using Data Quality Services to Cleanse Data

Lab:

Cleansing Data
Creating a DQS Knowledge Base
Using a DQS Project to Cleanse Data
Using DQS in an SSIS Package

After completing this module, you will be able to:

Describe how Data Quality Services can help you manage data quality
Use Data Quality Services to cleanse your data
Use Data Quality Services to match data

Module 10: Master Data Services

Master Data Services provides a way for organizations to standardize data and improve the quality, consistency, and reliability of the data that guides key business decisions. This module introduces Master Data Services and explains the benefits of using it.

Lessons

Introduction to Master Data Services
Implementing a Master Data Services Model
Managing Master Data
Creating a Master Data Hub

Lab:

Implementing Master Data Services
Creating a Master Data Services Model
Using the Master Data Services
Add-in for Excel Enforcing Business Rules
Loading Data Into a Model
Consuming Master Data Services Data

After completing this module, you will be able to:

Describe key Master Data Services concepts
Implement a Master Data Services model
Use Master Data Services tools to manage master data
Use Master Data Services tools to create a master data hub

Module 11: Extending SQL Server Integration Services

This module describes the techniques you can use to extend 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.

Lessons

Using Scripts in SSIS
Using Custom Components in SSIS

Lab:

Using Custom Scripts
Using a Script Task

After completing this module, you will be able to:

Include custom scripts in an SSIS package
Describe how custom components can be used to extend SSIS

Module 11: Deploying and Configuring SSIS Packages

In this module, students will learn how to deploy packages and their dependencies to a server, and how to manage and monitor the execution of deployed packages.

Lessons

Overview of SSIS Deployment
Deploying SSIS Projects
Planning SSIS Package Execution

Lab : Deploying and Configuring SSIS Packages

After completing this module, you will be able to:

Describe considerations for SSIS deployment.
Deploy SSIS projects.
Plan SSIS package execution.

Module 12: Consuming Data in a Data Warehouse

This module introduces business intelligence (BI) solutions and describes how you can use a data warehouse as the basis for enterprise and self-service BI.

Lessons

Introduction to Business Intelligence
Introduction to Reporting
An Introduction to Data Analysis

Lab : Using Business Intelligence Tools

After completing this module, you will be able to:

Describe BI and common BI scenarios
Describe how a data warehouse can be used in enterprise BI scenarios
Describe how a data warehouse can be used in self-service BI scenarios

More Information

More Information
Brand	Microsoft
Lab Access	Yes
Technology	Microsoft
Learning Style	Virtual Classroom
Difficulty	Intermediate
Course Duration	5 Days
Language	English
SATV Value	5 Days
VPA Eligible	VPA Eligible