Course 20767-C: Implementing a SQL Data Warehouse

Course Modality

Instructor-led (classroom)

Course Level

Advanced

Course Time

5 days

Course Language

English

Course Overview

This five-day instructor-led 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

Prerequisites

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

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

Intended Audience

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

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Skill Covered

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

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Course Curriculum

Introduction to Data Warehousing

This module describes data warehouse concepts and architecture consideration

Lessons

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

 

Lab: Exploring a Data Warehouse Solution

  • Exploring data sources
  • Exploring an ETL process
  • Exploring a data warehouse

 

After completing this module, student will be able to:

  • Describe the key elements of a data warehousing solution
  • Describe the key considerations for a data warehousing solution

Planning Data Warehouse Infrastructure

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

Lessons

  • Considerations for data warehouse infrastructure.
  • Planning data warehouse hardware.

 

Lab: Planning Data Warehouse Infrastructure

  • Planning data warehouse hardware

 

After completing this module, students will be able to:

  • 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

Designing and Implementing a Data Warehouse

This module describes how you go about designing and implementing a schema for a data warehouse.

Lessons

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

 

Lab: Implementing a Data Warehouse Schema

  • Implementing a star schema
  • Implementing a snowflake schema
  • Implementing a time dimension table

 

After completing this module, students will be able to: 

  • Implement a logical design for a data warehouse
  • Implement a physical design for a data warehouse

Columnstore Indexes

This module introduces Columnstore Indexes.

Lessons

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

 

Lab: Using Columnstore Indexes

  • Create a Columnstore index on the FactProductInventory table
  • Create a Columnstore index on the FactInternetSales table
  • Create a memory optimized Columnstore table

 

After completing this module, students will be able to: 

  • Create Columnstore indexes
  • Work with Columnstore Indexes

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
  • Copying data with the Azure data factory

 

Lab: Implementing an Azure SQL Data Warehouse

  • Create an Azure SQL data warehouse database
  • Migrate to an Azure SQL Data warehouse database
  • Copy data with the Azure data factory

 

After completing this module, students will be able to: 

  • Describe the advantages of Azure SQL Data Warehouse
  • Implement an Azure SQL Data Warehouse
  • Describe the considerations for developing an Azure SQL Data Warehouse
  • Plan for migrating to Azure SQL Data Warehouse

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

  • Exploring source data
  • Transferring data by using a data row task
  • Using transformation components in a data row

 

After completing this module, students will be able to: 

  • Describe ETL with SSIS
  • Explore Source Data
  • Implement a Data Flow

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
  • Managing consistency.

 

Lab: Implementing Control Flow in an SSIS Package

  • Using tasks and precedence in a control flow
  • Using variables and parameters
  • Using containers

 

Lab : Using Transactions and Checkpoints 

  • Using transactions
  • Using checkpoints

 

After completing this module, students will be able to: 

  • Describe control flow
  • Create dynamic packages
  • Use containers

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

  • Debugging an SSIS package
  • Logging SSIS package execution
  • Implementing an event handler
  • Handling errors in data flow

 

After completing this module, students will be able to: 

  • Debug an SSIS package
  • Log SSIS package events
  • Handle errors in an SSIS package

Implementing a Data Extraction Solution

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
  • Loading modified data
  • Temporal Tables

 

Lab: Extracting Modified Data

  • Using a datetime column to incrementally extract data
  • Using change data capture
  • Using the CDC control task
  • Using change tracking

 

Lab : Loading a data warehouse

  • Loading data from CDC output tables
  • Using a lookup transformation to insert or update dimension data
  • Implementing a slowly changing dimension
  • Using the merge statement

 

After completing this module, students will be able to: 

  • Describe incremental ETL
  • Extract modified data
  • Load modified data.
  • Describe temporal tables

Enforcing Data QualityThis

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 Data

  • Creating a DQS knowledge base
  • Using a DQS project to cleanse data
  • Using DQS in an SSIS package

 

Lab: De-duplicating Data

  • Creating a matching policy
  • Using a DS project to match data

 

After completing this module, students will be able to: 

  • Describe data quality services
  • Cleanse data using data quality services
  • Match data using data quality services
  • De-duplicate data using data quality services

Using Master Data Services

This module explains how an information technology (IT) administrator can account for and mitigate the risks of malicious code, unauthorized use, and data theft.

Lessons

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

 

Lab : Implementing Master Data Services

  • Creating a master data services model
  • Using the master data services add-in for Excel
  • Enforcing business rules
  • Loading data into a model
  • Consuming master data services data

 

After completing this module, students will be able to: 

  • Describe the key concepts of master data services
  • Implement a master data service model
  • Manage master data
  • Create a master data hub

Extending SQL Server Integration Services (SSIS)

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

Lessons

  • Using scripting in SSIS
  • Using custom components in SSIS

 

Lab: Using scripts

  • Using a script task

 

After completing this module, students will be able to: 

  • Use custom components in SSIS
  • Use scripting in SSIS

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

  • Creating an SSIS catalog
  • Deploying an SSIS project
  • Creating environments for an SSIS solution
  • Running an SSIS package in SQL server management studio
  • Scheduling SSIS packages with SQL server agent

 

After completing this module, students will be able to: 

  • Describe an SSIS deployment
  • Deploy an SSIS package
  • Plan SSIS package execution

Consuming Data in a Data Warehouse

This module describes how to debug and troubleshoot SSIS packages.

Lessons

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

 

Lab: Using a data warehouse

  • Exploring a reporting services report
  • Exploring a PowerPivot workbook
  • Exploring a power view report

 

After completing this module, students will be able to: 

  • 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

Recommended Exams

Exam 70-767

Implementing a Data Warehouse using SQL

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