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20767: Implementing a SQL Data Warehouse

Implementing a SQL Data Warehouse

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  • May 20, 2019 - May 24, 2019
    9:00 am - 5:00 pm

About this Course

About this course

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.

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.

Audience profile

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.

At course completion

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 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

About this course

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 profile

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.

At course completion

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 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 Details

Course Outline

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

Audience Profile

At Course Completion

Outline

Prerequisites

Venue:  

Address:
Online Event

Description:

Live Online Training

Get the same training you expect in the classroom without leaving your office or home. These are NOT recorded classes. They are LIVE sessions with an expert instructor. We use the latest in video conferencing technologies and audio so you can confidently participate in any class just like being right there in person. We guarantee the effectiveness of our online training delivery approach that we will give you your money back if you are not totally satisfied.  Ask us for a demo.

Online class requirements:

  • Moderate to fast Internet
  • Phone, or Speakers or headset (required in order to hear the instructor/moderator). You can use Voice Over IP (VoIP) or you can dial-in from a regular phone.  Whether you decide to use VoIP or a phone, for convenience, we recommend a hands free headset.
  • Training software must be installed on your computer (30 trial versions are acceptable)
  • RECOMMENDED: Dual Monitors or computers. For optimal online learning experience, we recommend participants have dual monitors or dual computers. Your online classroom credentials allow you to login multiple times from multiple computers. Participants should use one monitor or computer to view instructor/moderator shared screen and another monitor/computer to work in the software.

What happens when you enroll in an online class

When you register for an online class, you will receive a welcome email followed by login access to the Citrix GoToTraining virtual classroom. A workbook (printed copy or eBook) will be sent to you prior to the start of class.

Online Training Advantages

Convenience: You don’t have to travel and can attend from your home, office or anywhere with an internet connection. Our online classes are conducted using GoToTraining, a more robust version of the popular GoToMeeting screen sharing and conferencing platform. To accommodate multiple time zones, courses are typically scheduled from 10am – 5pm Eastern with a one-hour lunch break at 12:30 – 1:30 pm Eastern and a 10-minute break in the morning and afternoon. When conducting custom online course for your group, class times can be modified to accommodate your timezone.

Interactive Learning: Our online training is fully interactive. You can speak and chat with the instructor and classmates at any time. Various interactive techniques are used in every class. Our  small class sizes (typically 4 – 8 students), allow our instructors to focus on individual performance and issues and to work closely with you to meet your unique needs. Classes are designed to be a hands-on learning experience, providing opportunities for you to try your new skills while the instructor is available for review, questions, and feedback. You have the option to give the instructor permission to view your computer to provide one-on-one assistance when needed.