M20767 Implementing a SQL 2016 Data Warehouse

M20767

This 5 day instructor led M20767 course on how to implement a data warehouse platform to support a BI solution. You will learn to create a data warehouse with SQL Server 2016 & Azure SQL Data Warehouse, to implement ETL with SQL Server Integration Services, & validate and cleanse data with SQL Server Data Quality Services and SQLServer Master Data Services.

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This course is aimed at:

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.

You will need:

  • Basic knowledge of the Microsoft Windows operating system and its core functionality.
  • Working knowledge of relational databases.
  • Some experience with database design

Learning Outcomes:

  • 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:

Module 1: Introduction to Data Warehousing Describe data warehouse concepts and architecture considerations.

Lessons

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

Lab : Exploring a Data Warehouse Solution

Module 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

Lab : Planning Data Warehouse Infrastructure

Module 3: Designing and Implementing a Data Warehouse This module describes how you go about designing and implementing a schema for a data warehouse.

Lessons

  • Logical Design for a Data Warehouse
  • Physical Design for a Data Warehouse

Lab : Implementing a Data Warehouse Schema

Module 4: Columnstore Indexes This module introduces Columnstore Indexes.

Lessons

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

Lab : Using Columnstore Indexes

Module 5: Implementing an Azure SQL Data Warehouse This module describes Azure SQL Data Warehouses and how to implement them.

Lessons

  • 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 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

Lab : Implementing Data Flow in an SSIS Package

Module 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

Lab : Implementing Control Flow in an SSIS PackageLab : Using Transactions and Checkpoints

Module 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

Lab : Debugging and Troubleshooting an SSIS Package

Module 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

Lab : Extracting Modified DataLab : Loading Incremental Changes

Module 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

Lab : Cleansing DataLab : De-duplicating Data

Module 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

Lab : Implementing Master Data Services

Module 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

Lab : Using Scripts and Custom Components

Module 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

Lab : Deploying and Configuring SSIS Packages

Module 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

Lab : Using Business Intelligence Tools

£ 2,195.00 ex.vat

Data sheet

Course Duration 5 Days
Location Various