Implementing a SQL Data Warehouse

Microsoft

Microsoft

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Code

20767

Revision

B

Duration

5 Days

Audience

IT Professionals

Level

Expert

Price

$3,500.00

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.

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.

After completing this course, students will be able to:

  • 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

In addition to their professional experience, students who attend this training should already have the following technical knowledge:

  • 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.

1 Introduction to Data Warehousing

Describe data warehouse concepts and architecture considerations.

Lessons
  • Overview of Data Warehousing
  • Considerations for a Data Warehouse Solution
Labs
  • Describe the key elements of a data warehousing solution
  • Describe the key considerations for a data warehousing solution

2 Planning Data Warehouse Infrastructure

This module describes the main hardware considerations for building a data warehouse.

Lessons
  • Considerations for Building a Data Warehouse
  • Data Warehouse Reference Architectures and Appliances
Labs
  • Describe the main hardware considerations for building a data warehouse
  • Explain how to use reference architectures and data warehouse appliances to create a data warehouse

3 Planning Data Warehouse Infrastructure

This module describes the main hardware considerations for building a data warehouse.

Lessons
  • Considerations for Building a Data Warehouse
  • Data Warehouse Reference Architectures and Appliances
Labs
  • Describe the main hardware considerations for building a data warehouse
  • Explain how to use reference architectures and data warehouse appliances to create a data warehouse

4 Planning Data Warehouse Infrastructure

This module describes the main hardware considerations for building a data warehouse.

Lessons
  • Considerations for Building a Data Warehouse
  • Data Warehouse Reference Architectures and Appliances
Labs
  • Describe the main hardware considerations for building a data warehouse
  • Explain how to use reference architectures and data warehouse appliances to create a data warehouse

5 Planning Data Warehouse Infrastructure

This module describes the main hardware considerations for building a data warehouse.

Lessons
  • Considerations for Building a Data Warehouse
  • Data Warehouse Reference Architectures and Appliances
Labs
  • Describe the main hardware considerations for building a data warehouse
  • Explain how to use reference architectures and data warehouse appliances to create a data warehouse

6 Creating an ETL Solution

At the end of this module you will be able to implement data flow in a SSIS package.

Lessons
  • Introduction to ETL with SSIS
  • Exploring Source Data
  • Implementing Data Flow
Labs
  • Describe ETL with SSIS
  • Explore Source Data
  • Implement a Data Flow

7 Implementing Control Flow in an SSIS Package

This module describes implementing control flow in an SSIS package.

Lessons
  • Introduction to Control Flow
  • Creating Dynamic Packages
  • Using Containers
Labs
  • Describe control flow
  • Create dynamic packages
  • Use containers

8 Debugging and Troubleshooting SSIS Packages

This module describes how to debug and troubleshoot SSIS packages.

Lessons
  • Debugging an SSIS Package
  • Logging SSIS Package Events
  • Handling Errors in an SSIS Package
Labs
  • Debug an SSIS package
  • Log SSIS package events
  • Handle errors in an SSIS package

9 Implementing an Incremental ETL Process

This module describes how to implement an SSIS solution that supports incremental DW loads and changing data.

Lessons
  • Introduction to Incremental ETL
  • Extracting Modified Data
  • Temporal Tables
Labs
  • Describe incremental ETL
  • Extract modified data
  • Describe temporal tables

10 Enforcing Data Quality

This module describes how to implement data cleansing by using Microsoft Data Quality services.

Lessons
  • Introduction to Data Quality
  • Using Data Quality Services to Cleanse Data
  • Using Data Quality Services to Match Data
Labs
  • Describe data quality services
  • Cleanse data using data quality services
  • Match data using data quality services
  • De-duplicate data using data quality services

11 Using Master Data Services

This module describes how to implement master data services to enforce data integrity at source.

Lessons
  • Master Data Services Concepts
  • Implementing a Master Data Services Model
  • Managing Master Data
  • Creating a Master Data Hub
Labs
  • Describe the key concepts of master data services
  • Implement a master data service model
  • Manage master data
  • Create a master data hub

12 Extending SQL Server Integration Services (SSIS)

This module describes how to extend SSIS with custom scripts and components.

Lessons
  • Using Custom Components in SSIS
  • Using Scripting in SSIS
Labs
  • Use custom components in SSIS
  • Use scripting in SSIS

13 Deploying and Configuring SSIS Packages

This module describes how to deploy and configure SSIS packages.

Lessons
  • Overview of SSIS Deployment
  • Deploying SSIS Projects
  • Planning SSIS Package Execution
Labs
  • Describe an SSIS deployment
  • Deploy an SSIS package
  • Plan SSIS package execution

14 Consuming Data in a Data Warehouse

This module describes how to debug and troubleshoot SSIS packages.

Lessons
  • Introduction to Business Intelligence
  • Introduction to Reporting
  • An Introduction to Data Analysis
  • Analyzing Data with Azure SQL Data Warehouse
Labs
  • Describe at a high level business intelligence
  • Show an understanding of reporting
  • Show an understanding of data analysis
  • Analyze data with Azure SQL data warehouse