Implementing a SQL Data Warehouse – SSIS 20767C

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 2016 provision both on-premise and in Azure, and covers installing from new and migrating from an existing install. 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.

• 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

• 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.

• 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

• Create Columnstore indexes
• Work with Columnstore Indexes

Module 5: Implementing an Azure SQL Data Warehouse

This module describes Azure SQL Data Warehouses and how to implement them.

• 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.

• 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.

• 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

• 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.

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

Module 10: Enforcing Data Quality

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

• 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.

• 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.

• 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.

• 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.

• 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

Implementing a SQL Data Warehouse - SSIS Course 20767C 5 Days Instructor-led, Hands on Upon successful completion of 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

data cleansing.

• 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
• 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
• Implement Data Quality Services
• Implement a Master Data Services model
• Describe how you can use custom components to extend SSIS
• Deploy SSIS projects

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