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Implementing a SQL Data Warehouse

  • Course Code M20767
  • Duration 5 days

Public Classroom Price

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Course Delivery

This course is available in the following formats:

  • Company Event

    Event at company

  • Elearning (Self-paced)

    Self paced electronic learning

  • Public Classroom

    Traditional Classroom Learning

  • Virtual Learning

    Learning that is virtual

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Course Overview

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This 5-day instructor led 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® 2016 and with Azure SQL Data Warehouse, to implement ETL with SQL Server Integration Services, and to validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services.

Course Schedule

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    • Delivery Format: Public Classroom
    • Date: 03-07 February, 2019
    • Location: Cairo-Sheraton

    EGP0.00

    • Delivery Format: Public Classroom
    • Date: 01-05 September, 2019
    • Location: Cairo-Sheraton

    EGP0.00

Target Audience

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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.

Course Objectives

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  • Describe the key elements of a data warehousing solution
  • Describe the main hardware considerations for building a data warehouse
  • Implement a logical design for a data warehouse
  • Implement a physical design for a data warehouse
  • Create columnstore indexes
  • Implementing an Azure SQL Data Warehouse
  • Describe the key features of SSIS
  • Implement a data flow by using SSIS
  • Implement control flow by using tasks and precedence constraints
  • Create dynamic packages that include variables and parameters
  • Debug SSIS packages
  • Describe the considerations for implement an ETL solution
  • Implement Data Quality Services
  • Implement a Master Data Services model
  • Describe how you can use custom components to extend SSIS
  • Deploy SSIS projects
  • Describe BI and common BI scenarios

Course Content

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  • Module 1: Introduction to Data Warehousing
  • Overview of Data Warehousing
  • Considerations for a Data Warehouse Solution
  • Lab : Exploring a Data Warehouse Solution
  • Module 2: Planning Data Warehouse Infrastructure
  • Considerations for Building a Data Warehouse
  • Data Warehouse Reference Architectures and Appliances
  • Lab : Planning Data Warehouse Infrastructure
  • Module 3: Designing and Implementing a Data Warehouse
  • Logical Design for a Data Warehouse
  • Physical Design for a Data Warehouse
  • Lab : Implementing a Data Warehouse Schema
  • Module 4: Columnstore Indexes
  • Introduction to Columnstore Indexes
  • Creating Columnstore Indexes
  • Working with Columnstore Indexes
  • Lab : Using Columnstore Indexes
  • Module 5: Implementing an Azure SQL Data Warehouse
  • Advantages of Azure SQL Data Warehouse
  • Implementing an Azure SQL Data Warehouse
  • Developing an Azure SQL Data Warehouse
  • Migrating to an Azure SQ Data Warehouse
  • Lab : Implementing an Azure SQL Data Warehouse
  • Module 6: Creating an ETL Solution
  • Introduction to ETL with SSIS
  • Exploring Source Data
  • Implementing Data Flow
  • Lab : Implementing Data Flow in an SSIS Package
  • Module 7: Implementing Control Flow in an SSIS Package
  • Introduction to Control Flow
  • Creating Dynamic Packages
  • Using Containers
  • Lab : Implementing Control Flow in an SSIS Package
  • Lab : Using Transactions and Checkpoints
  • Module 8: Debugging and Troubleshooting SSIS Packages
  • Debugging an SSIS Package
  • Logging SSIS Package Events
  • Handling Errors in an SSIS Package
  • Lab : Debugging and Troubleshooting an SSIS Package
  • Module 9: Implementing an Incremental ETL Process
  • Introduction to Incremental ETL
  • Extracting Modified DataTemporal Tables
  • Lab : Extracting Modified Data
  • Lab : Loading Incremental Changes
  • Module 10: Enforcing Data Quality
  • 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
  • Module 11: Using Master Data Services
  • Master Data Services Concepts
  • Implementing a Master Data Services Model
  • Managing Master Data
  • Creating a Master Data Hub
  • Lab : Implementing Master Data Services
  • Module 12: Extending SQL Server Integration Services (SSIS)
  • Using Custom Components in SSIS
  • Using Scripting in SSIS
  • Lab : Using Scripts and Custom Components
  • Module 13: Deploying and Configuring SSIS Packages
  • Overview of SSIS Deployment
  • Deploying SSIS Projects
  • Planning SSIS Package Execution
  • Lab : Deploying and Configuring SSIS Packages
  • Module 14: Consuming Data in a Data Warehouse
  • Introduction to Business Intelligence
  • Introduction to Reporting
  • An Introduction to Data Analysis
  • Analyzing Data with Azure SQL Data Warehouse
  • Lab : Using Business Intelligence Tools

Course Prerequisites

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  • 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.