Data Warehousing Implementing a Microsoft SQL 2016 Data Warehouse (MS-20767)

Implementing a Microsoft SQL 2016 Data Warehouse (MS-20767)

Location:

Date of Class:

Instructor:

Last Day To Enroll:

2595.00 2,595.00
$2,595.00
PRICE PER USER
$
X
USERS


=
SUBTOTAL
$
  • Course Delivery: Virtual Classroom
  • Duration: 4 
  • Language: English
  • Audience: IT Professionals

Chat Live | Contact Us | Toll Free: (888) 360-8764

More About This Course
  • 100% Satisfied or your money back
  • Cancellation Free Schedule
  • Version Upgrade Discount
  • Corporate Resource Guarantee
  • Software Assurance Value: 4
 

Description

This 4-day instructor led course describes how to implement a data warehouse platform to support a BI solution. Students will 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.

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.  
 

Course Objectives

After completing 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
  • Implement a logical design for a data warehouse
  • Implement a physical design for a data warehouse
  • Create column store 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
 

Topics Covered

Module 1: Introduction to Data Warehousing
Describe data warehouse concepts and architecture considerations

Lessons
  • Overview of Data Warehousing
  • Considerations for a Data Warehouse Solution

Lab: Exploring a Data Warehouse Solution

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 Building a Data Warehouse
  • Data Warehouse Reference Architectures and Appliances

Lab: Planning Data Warehouse Infrastructure

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

Lab: Implementing a Data Warehouse Schema

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

After completing this module, you will be able to:
  • 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.

Lessons
  • 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

Lab: Implementing an Azure SQL Data Warehouse

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

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

Lab: Implementing Control Flow in an SSIS Package

Lab: Using Transactions and 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

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 an Incremental ETL Process
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
  • Temporal Tables

Lab: Extracting Modified Data

Lab: Loading Incremental Changes

After completing this module, you will be able to:
  • 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.

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

Lab: Cleansing Data

Lab: De-duplicating 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
  • Master Data Services Concepts
  • Implementing a Master Data Services Model
  • Managing Master Data
  • Creating a Master Data Hub

Lab: Implementing Master Data Services

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 Custom Components in SSIS
  • Using Scripting in SSIS

Lab: Using Scripts and Custom Components

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

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
  • Introduction to Reporting
  • An Introduction to Data Analysis
  • Analyzing Data with Azure SQL Data Warehouse

Lab: Using Business Intelligence Tools

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
 

Prerequisites

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

Subject Matter Expert

360training, powered by QuickStart brings a quarter-century of keeping pace with the ever-evolving IT industry. You will find that experience reflected in every course, and every training modality we offer. We train more than 11,000 IT professionals and developers annually, and 97% of them say they are very glad they chose us.

Got questions? Contact us below or call 877-881-2235

Why Choose 360training.com?

  • Fast and easy courses completion
  • Get an education faster than at traditional colleges!
  • 100% online - No classroom attendance required.
  • Unlimited 24x7 online customer support
  • Over 500,000+ certified nationwide.