Course OD20463D: Implementing a Data Warehouse with Microsoft® SQL Server® 2014

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Anytime accessYesNo
Anywhere access to recorded instructorYesNo
Microsoft official training contentYesYes
In-depth trainingYesYes
Hands-on labs Yes Yes
SATV redemption Yes Yes
Ask instructor questions in personNoYes
Attend live class in personNoYes
Attend live class remotelyNoYes
Time commitment Self-paced
(3 month access)
5 days
About this course
Audience(s):IT Professionals
Technology:Microsoft SQL Server
Level:300
This Revision:D
Delivery method:
Online
Length:2 days
Language(s):English

First published:

28 April 2016
Overview
About this course

This 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.

Audience profile

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.
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 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.





































    Course details
    Course OutlineModule 1: Introduction to Data WarehousingThis 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 ConsiderationsThis 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 WarehouseThis 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 SSISThis 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 PackageThis 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 PackageLab : 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 PackagesThis 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 ProcessThis 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 DataLab : Loading Incremental Changes

    After completing this module, you will be able to:

    • Plan data extraction
    • Extract modified data
    Module 8: Enforcing Data QualityThis 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 DataLab : 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 ServicesMaster 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 ServicesThis 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 PackagesIn 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 WarehouseThis 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
    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.
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