Implementing a SQL Data Warehouse


Description
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.
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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Module 1: Introduction to Data Warehousing
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Overview of Data Warehousing
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Considerations for a Data Warehouse Solution
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Lab : Exploring a Data Warehouse Solution
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Module 2: Planning Data Warehouse Infrastructure
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Considerations for data warehouse infrastructure.
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Planning data warehouse hardware.
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Lab : Planning Data Warehouse Infrastructure
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Module 3: Designing and Implementing a Data Warehouse
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Data warehouse design overview
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Designing dimension tables
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Designing fact tables
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Physical Design for a Data Warehouse
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Lab : Implementing a Data Warehouse Schema
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Module 4: Columnstore Indexes
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Introduction to Columnstore Indexes
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Creating Columnstore Indexes
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Working with Columnstore Indexes
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Lab : Using Columnstore Indexes
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Module 5: Implementing an Azure SQL Data Warehouse
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Advantages of Azure SQL Data Warehouse
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Implementing an Azure SQL Data Warehouse
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Developing an Azure SQL Data Warehouse
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Migrating to an Azure SQ Data Warehouse
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Copying data with the Azure data factory
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Lab : Implementing an Azure SQL Data Warehouse
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Module 6: Creating an ETL Solution
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Introduction to ETL with SSIS
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Exploring Source Data
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Implementing Data Flow
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Lab : Implementing Data Flow in an SSIS Package
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Module 7: Implementing Control Flow in an SSIS Package
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Introduction to Control Flow
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Creating Dynamic Packages
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Using Containers
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Managing consistency.
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Lab : Implementing Control Flow in an SSIS Package
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Lab : Using Transactions and Checkpoints
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Module 8: Debugging and Troubleshooting SSIS Packages
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Debugging an SSIS Package
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Logging SSIS Package Events
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Handling Errors in an SSIS Package
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Lab : Debugging and Troubleshooting an SSIS Package
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Module 9: Implementing a Data Extraction Solution
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Introduction to Incremental ETL
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Extracting Modified Data
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Loading modified data
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Temporal Tables
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Lab : Extracting Modified Data
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Lab : Loading a data warehouse
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Module 10: Enforcing Data Quality
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Introduction to Data Quality
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Using Data Quality Services to Cleanse Data
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Using Data Quality Services to Match Data
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Lab : Cleansing Data
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Lab : De-duplicating Data
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Module 11: Using Master Data Services
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Introduction to Master Data Services
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Implementing a Master Data Services Model
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Hierarchies and collections
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Creating a Master Data Hub
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Lab : Implementing Master Data Services
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Module 12: Extending SQL Server Integration Services (SSIS)
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Using scripting in SSIS
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Using custom components in SSIS
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Lab : Using scripts
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Module 13: Deploying and Configuring SSIS Packages
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Overview of SSIS Deployment
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Deploying SSIS Projects
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Planning SSIS Package Execution
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Lab : Deploying and Configuring SSIS Packages
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Module 14: Consuming Data in a Data Warehouse
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Introduction to Business Intelligence
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An Introduction to Data Analysis
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Introduction to reporting
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Analyzing Data with Azure SQL Data Warehouse
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Lab : Using a data warehouse
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- Basic knowledge of the Microsoft Windows operating system and its core functionality.
- Working knowledge of relational databases.
- Some experience with database design.
- 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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