Implementing a SQL Data Warehouse

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.
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.
AT COURSE COMPLETION
After completing this course, students will be able to:
 Provision a Database Server.
 Upgrade SQL Server.
 Configure SQL Server.
 Manage Databases and Files (shared).
COURSE OUTLINE
Module 1: 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, you will be able to:
 Describe the key elements of a data warehousing solution
 Describe the key considerations for a data warehousing solution
Module 2: 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, you 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
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
 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, you will be able to:
 Implement a logical design for a data warehouse
 Implement a physical design for a data warehouse
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
 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, you will be able to:
 Create Columnstore indexes

rehouse database
 Migrate to an Azure SQL Data warehouse database
 Copy data with the Azure data factory
After completing this module, you 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
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
 Exploring source data
 Transferring data by using a data row task
 Using transformation components in a data row
After completing this module, you will be able to:
 Describe ETL with SSIS
 Explore Source Data

Implement a Data Flow
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
 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, you will be able to:
 Describe control flow
 Create dynamic packages
 Use containers
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
 Debugging an SSIS package
 Logging SSIS package execution
 Implementing an event handler
 Handling errors in data flow
After completing this module, you will be able to:
 Debug an SSIS package

Log SSIS package events
 Handle errors in an SSIS package
Module 9: 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, you will be able to:
 Describe incremental ETL
 Extract modified data
 Load modified data.
 Describe temporal tables
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 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, you 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
Module 11: Using Master Data Services
This module describes how to implement master data services to enforce data integrity at source.
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, you 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
Module 12: 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, you will be able to:
 Use custom components in SSIS
 Use scripting in SSIS
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
 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, you will be able to:
 Describe an SSIS deployment
 Deploy an SSIS package
 Plan SSIS package execution
Module 14: 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, you 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