20767: Implementing SQL Data Warehouse Training

20767: Implementing SQL Data Warehouse Training teaches professionals the leading methodologies to build a data warehouse with the help of MS SQL Server and Azure SQL Data Warehouse. This technical course demonstrates how to apply a data warehouse platform to support a BI solution. It provides comprehensive knowledge of the main hardware considerations for developing a data warehouse. Throughout this course, trainees will learn to implement a logical design and a physical design for a data warehouse. It also elaborates on the critical features of SSIS and teaches how to create dynamic packages, including parameters and variables. This training program is ideal for a data professional who works as a Business Intelligence Developer. This program covers all the essential topics from implementing control flow and debugging SSIS packages to applying to a master data services school.

This training is designed based on the objectives of the course variant 20767C.

• Describing data warehousing solution’s key elements.
• Describe the major hardware considerations to build the data warehouse
• Implementing a logical and physical design for the data warehouses
• Creating column store indexes
• Implement the Azure SQL Data Warehouse
• Describing SSIS’s key features
• Using SSIS for implementing data flow
• Using precedence constraints and tasks for implementing control flow
• Create variables and parameters consisting of dynamic packages
• Debugging SSIS packages
• Describing the considerations for implementing ETL solution
• Implementing Data Quality Services
• Implementing the Master Data Services model
• Describe using of custom components for extending SSIS
• Deploying SSIS projects
• Describing Business Intelligence and common Business Intelligence scenarios

The database professionals looking to upgrade to a BI developer role are the primary audience for the course. This course will teach them hands-on work to create Business Intelligence solutions that include implementing Data Warehouse, data cleansing and ETL.

• Knowledge of Microsoft Windows operating system; and, its core functionality
• Knowledge of relational databases
• Experience in database designing

• 1. Introduction to Data Warehousing

This module describes data warehouse concepts and architecture consideration.

• 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
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2. Planning Data Warehouse Infrastructure
This module describes the main hardware considerations for building a data warehouse.
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
3. Designing and Implementing a Data Warehouse
This module describes how you go about designing and implementing a schema for a data warehouse.
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, you will be able to:
Implement a logical design for a data warehouse
Implement a physical design for a data warehouse
4. Columnstore Indexes
This module introduces Columnstore Indexes.
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
Work with Columnstore Indexes
5. Implementing an Azure SQL Data Warehouse
This module describes Azure SQL Data Warehouses and how to implement them.
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, 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
6. Creating an ETL Solution
At the end of this module you will be able to implement data flow in a SSIS package.
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
7. Implementing Control Flow in an SSIS Package
This module describes implementing control flow in an SSIS package.
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
8. Debugging and Troubleshooting SSIS Packages
This module describes how to debug and troubleshoot SSIS packages.
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
9. Implementing a Data Extraction Solution
This module describes how to implement an SSIS solution that supports incremental DW loads and changing data.
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:
Extract modified data
Load modified data.
Describe temporal tables
10. Enforcing Data Quality
This module describes how to implement data cleansing by using Microsoft Data Quality services.
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
11. Using Master Data Services
This module describes how to implement master data services to enforce data integrity at source.
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
12. Extending SQL Server Integration Services (SSIS)
This module describes how to extend SSIS with custom scripts and components.
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
13. Deploying and Configuring SSIS Packages
This module describes how to deploy and configure SSIS packages.
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
Plan SSIS package execution
14. Consuming Data in a Data Warehouse
This module describes how to debug and troubleshoot SSIS packages.
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