Implementing a SQL Data Warehouse (M20767) Course Details:

In this course, provides students with the knowledge and skills to provision a Microsoft SQL Server database. The course covers SQL Server provision both on-premise and in Azure, and covers installing from new and migrating from an existing install. This course incorporates material from the Official Microsoft Learning Product 20767: and it can assist you in your preparation for Exam 70-767: Implementing a SQL Data Warehouse.

    Oct 18 2021

    Date: 10/18/2021 - 10/22/2021 (Monday - Friday) | - (EST)
    Location: ONLINE (Virtual Classroom Live)
    Delivery Format: VIRTUAL CLASSROOM LIVE Request Quote & Enroll

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    Implementing a SQL Data Warehouse (M20767)

    October 18 - 22, 2021 | - (EST) | Virtual Classroom Live


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    Nov 1 2021

    Date: 11/01/2021 - 11/05/2021 (Monday - Friday) | 11:30 AM - 7:30 PM (EST)
    Location: ONLINE (Virtual Classroom Live)
    Delivery Format: VIRTUAL CLASSROOM LIVE Request Quote & Enroll

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    Implementing a SQL Data Warehouse (M20767)

    November 1 - 5, 2021 | 11:30 AM - 7:30 PM (EST) | Virtual Classroom Live


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    Nov 15 2021

    Date: 11/15/2021 - 11/19/2021 (Monday - Friday) | 8:30 AM - 4:30 PM (EST)
    Location: ONLINE (Virtual Classroom Live)
    Delivery Format: VIRTUAL CLASSROOM LIVE Request Quote & Enroll

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    Implementing a SQL Data Warehouse (M20767)

    November 15 - 19, 2021 | 8:30 AM - 4:30 PM (EST) | Virtual Classroom Live


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    Dec 13 2021

    Date: 12/13/2021 - 12/17/2021 (Monday - Friday) | 8:30 AM - 4:30 PM (EST)
    Location: ONLINE (Virtual Classroom Live)
    Delivery Format: VIRTUAL CLASSROOM LIVE Request Quote & Enroll

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    Implementing a SQL Data Warehouse (M20767)

    December 13 - 17, 2021 | 8:30 AM - 4:30 PM (EST) | Virtual Classroom Live


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1. Introduction to Data Warehousing

  • Overview of Data Warehousing
  • Considerations for a Data Warehouse Solution

2. Planning Data Warehouse Infrastructure

  • Considerations for Building a Data Warehouse
  • Planning data warehouse hardware

3. Designing and Implementing a Data Warehouse

  • Data warehouse design overview
  • Designing dimension tables
  • Designing fact tables
  • Physical Design for a Data Warehouse

4. Columnstore Indexes

  • Introduction to Columnstore Indexes
  • Creating Columnstore Indexes
  • Working with Columnstore Indexes

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
  • Copying data with Azue data factory

6. Creating an ETL Solution

  • Introduction ETL with SSIS
  • Exploring Source Data
  • Implementing Data Flow

7. Implementing Control Flow in an SSIS Package

  • Introduction to Control Flow
  • Creating Dynamic Packages
  • Using Containers
  • Managing consistency

8. Debugging and Troubleshooting SSIS Packages

  • Debugging an SSIS Package
  • Logging SSIS Package Events
  • Handling Errors in Data Flow

9. Implementing a Data Extraction Solution

  • Introduction to Incremental ETL
  • Extracting Modified Data
  • Loading Modified Data
  • Temporal Tables

10. Enforcing Data Quality

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

11. Using Master Data Services

  • Introduction to Master Data Services Concepts
  • Implementing a Master Data Services Model
  • Hierarchies and Collections
  • Creating a Master Data Hub

12. Extending SQL Server Integration Services (SSIS)

  • Using Custom Components in SSIS
  • Using Scripting in SSIS

13. Deploying and Configuring SSIS Packages

  • Overview of SSIS Deployment
  • Deploying SSIS Projects
  • Planning SSIS Package Execution

14. Consuming Data in a Data Warehouse

  • Introduction to Business Intelligence
  • An Introduction to Data Analysis
  • Introduction to Reporting
  • Analyzing Data with Azure SQL Data Warehouse

*Please Note: Course Outline is subject to change without notice. Exact course outline will be provided at time of registration.
  • Describe the key elements of a data warehousing solution
  • Describe the main hardware consideration 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 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 Maser Data Services model
  • Describe how you can use custom components to extend SSIS
  • Deploy SSIS projects
  • Describe BI and common BI scenarios

Lab 1: Exploring a Data Warehouse Solution

Lab 2: Planning Data Warehouse Infrastructure

Lab 3: Implementing a Data Warehouse Schema

Lab 4: Using Columnstore Indexes

Lab 5: Implementing an Azure SQL Data Warehouse

Lab 6: Implementing Data Flow in an SSIS Package

Lab 7: Implementing Control Flow in an SSIS Package

Lab 8: Using Transactions and Checkpoints

Lab 9: Debugging and Troubleshooting an SSIS Package

Lab 10: Extracting Modified Data

Lab 11: Loading a data warehouse

Lab 12: Cleansing Data

Lab 13: De-duplicating Data

Lab 14: Implementing Master Data Services

Lab 15: Using Scripts

Lab 16: Deploying and Configuring SSIS Packages

Lab 17: Using a Data Warehouse

  • Basic knowledge of the Microsoft Windows operating system and its core functionality.
  • Working knowledge of relational databases.
  • Some experience with database design
  • Database professionals who need to fulfill a BI developer role focused on hands-on work by creating BI solutions, including data warehouse implementation, ETL and data cleansing
  • Database professionals responsible for implementing a data warehouse, developing SSIS packages for data ETL, enforcing data integrity using Microsoft Data Services and cleansing data using Data Quality Services

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