Implementing a Data Warehouse with Microsoft SQL Server 2014 (180 Day)

Course Details
Code: ODX20463
Tuition (USD): $1,070.00 $802.50 • Self Paced
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Instructor-Led Classroom & Virtual
Implementing a Data Warehouse with Microsoft SQL Server 2014 (20463)

Data warehousing is a solution organizations use to centralize business data for reporting and analysis. This course focuses on teaching individuals how to create a data warehouse with SQL Server 2014, and implement ETL with SQL Server Integration Services. This course helps people prepare for exam 70-463.

Who Can Benefit

This course is intended for databaseprofessionals who need to fulfil a Business Intelligence Developer role.  They will need to focus on hands-on workcreating BI solutions including Data Warehouse implementation, ETL, and datacleansing. 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.


Thiscourse requires that you meet the following prerequisites:

  • At least 2 yearsexperience of working with relational databases, including:
  • Designing anormalized database.
  • Creating tablesand relationships.
  • Querying withTransact-SQL.
  • Some exposure tobasic programming constructs (such as looping and branching).
An awareness of key business priorities such as revenue,profitability, and financial accounting is desirable.

Course Details


Module 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.

  • Overview of Data Warehousing
  • Considerations for a Data Warehouse Solution
Lab : Exploring a Data Warehousing SolutionAftercompleting 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.
  • Considerations for building a Data Warehouse
  • Data Warehouse Reference Architectures and Appliances
Lab : Planning Data Warehouse InfrastructureAfter completing this module, you will be able to:
  • Describe key considerations for BIinfrastructure.
  • 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.
  • Logical Design for a Data Warehouse
  • Physical design for a data warehouse
Lab : Implementing a Data Warehouse SchemaAftercompleting this module, you will be able to:
  • Describe a processfor designing a dimensional model for a data warehouse
  • Design dimensiontables for a data warehouse
  • Design fact tablesfor a data warehouse
  • Design and implement effective physical datastructures 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.
  • Introduction to ETL with SSIS
  • Exploring Data Sources
  • Implementing Data Flow
Lab : Implementing Data Flow in an SSIS PackageAfter completing thismodule, you will be able to:
  • Describe the keyfeatures of SSIS.
  • Explore sourcedata 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.
  • Introduction to Control Flow
  • Creating Dynamic Packages
  • Using Containers
  • Managing Consistency
Lab : Implementing Control Flow in an SSIS PackageLab : Using Transactions and CheckpointsAftercompleting this module, you will be able to:
  • Implement controlflow with tasks and precedence constraints
  • Create dynamicpackages that include variables and parameters
  • Use containers ina package control flow
  • Enforce consistency with transactions andcheckpoints
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.
  • Debugging an SSIS Package
  • Logging SSIS Package Events
  • Handling Errors in an SSIS Package
Lab : Debugging and Troubleshooting an SSIS PackageAfter completing this module, you will beable to:
  • Debug an SSISpackage
  • Implement loggingfor 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.
  • Introduction to Incremental ETL
  • Extracting Modified Data
  • Loading Modified data
Lab : Extracting Modified DataLab : Loading Incremental ChangesAfter completing this module, you will beable to:
  • Plan dataextraction
  • 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.
  • Introduction to Data Quality
  • Using Data Quality Services to Cleanse Data
  • Using Data Quality Services to Match data
Lab : Cleansing DataLab : De-duplicating dataAfter completing thismodule, you will be able to:
  • Describe how DataQuality Services can help you manage data quality
  • Use Data QualityServices 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.
  • Master Data Services Concepts
  • Implementing a Master Data Services Model
  • Managing Master Data
  • Creating a Master Data Hub
Lab : Implementing Master Data ServicesAfter completing thismodule, you will be able to:
  • Describe keyMaster Data Services concepts
  • Implement a MasterData Services model
  • Use Master DataServices tools to manage master data
  • Use Master Data Services tools to create amaster 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.
  • Using Scripts in SSIS
  • Using Custom Components in SSIS
Lab : Using Custom Components and ScriptsAfter completing this module, you will beable to:
  • Include customscripts in an SSIS package
  • Describe how custom components can be used toextend 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.
  • Overview of SSIS Deployment
  • Deploying SSIS Projects
  • Planning SSIS Package Execution
Lab : Deploying and Configuring SSIS PackagesAfter completing this module, you will beable to:
  • Describeconsiderations for SSIS deployment.
  • Deploy SSISprojects.
  • 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.
  • Introduction to Business Intelligence
  • Introduction to Reporting
  • An Introduction to Data Analysis
Lab : Using Business Intelligence ToolsAfter completing thismodule, you will be able to:
  • Describe BI andcommon BI scenarios
  • Describe how adata warehouse can be used in enterprise BI scenarios
  • Describe how a data warehouse can be used inself-service BI scenarios