Implementing a SQL Data Warehouse 2016 Training

Implementing a SQL Data Warehouse 2016 Training (M20767)

Introduction:

Implementing a SQL Data Warehouse 2016 Training Course Onsite and Virtual Classrooms

This 5-day, instructor-led Implementing a SQL Data Warehouse 2016 Training course describes how to implement a data warehouse platform to support a BI solution. With Implementing a SQL Data Warehouse 2016 Training, you will also learn how to create a data warehouse with Microsoft SQL Server 2016 and with Azure SQL Data Warehouse, to implement ETL with SQL Server Integration Services, and to validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services.

Customize It:

With onsite Training, courses can be scheduled on a date that is convenient for you, and because they can be scheduled at your location, you don’t incur travel costs and students won’t be away from home. Onsite classes can also be tailored to meet your needs. You might shorten a 5-day class into a 3-day class, or combine portions of several related courses into a single course, or have the instructor vary the emphasis of topics depending on your staff’s and site’s requirements.

Audience/Target Group

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.

Implementing a SQL Data Warehouse 2016 Training (M20767)Related Courses:

Duration: 5 days

Class Prerequisites:

At least 2 years’ experience of working with relational databases, including:
Designing a normalized database.
Creating tables and relationships.
Querying with Transact-SQL.
Some exposure to basic programming constructs (such as looping and branching).
An awareness of key business priorities such as revenue, profitability, and financial accounting is desirable.

What You Will Learn:

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

Course Content:

Module 1: Introduction to Data Warehousing

Overview of Data Warehousing
Considerations for a Data Warehouse Solution

Module 2: Planning Data Warehouse Infrastructure

Considerations for Building a Data Warehouse
Data Warehouse Reference Architectures and Appliances

Module 3: Designing and Implementing a Data Warehouse

Logical Design for a Data Warehouse
Physical Design for a Data Warehouse

Module 4: Columnstore Indexes

Introduction to Columnstore Indexes
Creating Columnstore Indexes
Working with Columnstore Indexes

Module 5: Implementing an Azure SQL Data Warehouse

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

Module 6: Creating an ETL Solution

Introduction to ETL with SSIS
Exploring Source Data
Implementing Data Flow

Module 7: Implementing Control Flow in an SSIS Package

Introduction to Control Flow
Creating Dynamic Packages
Using Containers

Module 8: Debugging and Troubleshooting SSIS Packages

Debugging an SSIS Package
Logging SSIS Package Events
Handling Errors in an SSIS Package

Module 9: Implementing an Incremental ETL Process

Introduction to Incremental ETL
Extracting Modified Data
Temporal Tables

Module 10: Enforcing Data Quality

Introduction to Data Quality
Using Data Quality Services to Cleanse Data
Using Data Quality Services to Match Data

Module 11: Using Master Data Services

Master Data Services Concepts
Implementing a Master Data Services Model
Managing Master Data
Creating a Master Data Hub

Module 12: Extending SQL Server Integration Services (SSIS)

Using Custom Components in SSIS
Using Scripting in SSIS

Module 13: Deploying and Configuring SSIS Packages

Overview of SSIS Deployment
Deploying SSIS Projects
Planning SSIS Package Execution

Module 14: Consuming Data in a Data Warehouse

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

Labs

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Time Frame: 0-3 Months4-12 Months

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