Implementing a Data Warehouse with Microsoft SQL Server 2014 - MOC On Demand (MS-20463)

This course from Microsoft provides learners with the knowledge and skills required to design, implement, and load a relational data warehouse.

Self-Paced Learning
Learning Style
Intermediate
Difficulty
2 Days
Course Duration
2 Days
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This course from Microsoft provides learners with the knowledge and skills required to design, implement, and load a relational data warehouse.

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 Objective:

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:

Career path:

IT Ops & Management Career Path...

Course Outline

Self-Paced Learning 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

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: Data Warehouse Hardware Considerations

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

Lessons

Considerations for building a Data Warehouse
Data Warehouse Reference Architectures and Appliances

Lab : Planning Data Warehouse Infrastructure

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

Logical Design for a Data Warehouse
Physical design for a data warehouse

Lab : Implementing a Data Warehouse Schema

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

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

Lab : Using Transactions and 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

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 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

Lab : Loading Incremental Changes

After completing this module, you will be able to:

Plan data extraction
Extract modified data

Module 8: Enforcing Data Quality

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

Lessons

Introduction to Data Quality
Using Data Quality Services to Cleanse Data
Using Data Quality Services to Match data

Lab : Cleansing Data

Lab : De-duplicating data

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 9: Using 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

Master Data Services Concepts
Implementing a Master Data Services Model
Managing Master Data
Creating a Master Data Hub

Lab : Implementing Master Data Services

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 10: 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 Components and Scripts

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	No
Technology	Microsoft
Learning Style	Self-Paced Learning
Difficulty	Intermediate
Course Duration	2 Days
Language	English
SATV Value	2 Days
VPA Eligible	VPA Eligible