20463: Implementing a Data Warehouse with Microsoft SQL Server 2014

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Description

About this course

This 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® 2014, implement ETL with SQL Server Integration Services, and validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services.

Note: This course is designed for customers who are interested in learning SQL Server 2012 or SQL Server 2014. It covers the new features in SQL Server 2014, but also the important capabilities across the SQL Server data platform.

 

Audience profile

This course is intended for 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. Primary responsibilities include:

  • Implementing a data warehouse.

  • Developing SSIS packages for data extraction, transformation, and loading.

  • Enforcing data integrity by using Master Data Services.

  • Cleansing data by using Data Quality Services.

 

At course completion

After completing this course, students will be able to:

  • Describe data warehouse concepts and architecture considerations.

  • Select an appropriate hardware platform for a data warehouse.

  • Design and implement a data warehouse.

  • Implement Data Flow in an SSIS Package.

  • Implement Control Flow in an SSIS Package.

  • Debug and Troubleshoot SSIS packages.

  • Implement an ETL solution that supports incremental data extraction.

  • Implement an ETL solution that supports incremental data loading.

  • Implement data cleansing by using Microsoft Data Quality Services.

  • Implement Master Data Services to enforce data integrity.

  • Extend SSIS with custom scripts and components.

  • Deploy and Configure SSIS packages.

  • Describe how BI solutions can consume data from the data warehouse.

 

Prerequisites

This course requires that you meet the following 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.

 

Course Outline

 

Module 1: Introduction to Data Warehousing

This module provides an introduction to the key components of a data warehousing solution and the high-level considerations you must take into account when you embark on a data warehousing project.

 

Lessons

Overview of Data Warehousing

Considerations for a Data Warehouse Solution

 

Lab: Exploring a Data Warehousing 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 project

 

Module 2: Data Warehouse Hardware Considerations

This module discusses considerations for selecting hardware and distributing SQL Server facilities across servers.

 

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 key considerations for BI infrastructure.

Plan data warehouse infrastructure.

 

Module 3: Designing and Implementing a Data Warehouse

This module describes the key considerations for the logical design of a data warehouse, and then discusses best practices for its physical implementation.

 

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:

Describe a process for designing a dimensional model for a data warehouse

Design dimension tables for a data warehouse

Design fact tables for a data warehouse

Design and implement effective physical data structures for a data warehouse

 

Module 4: Creating an ETL Solution with SSIS

This module discusses considerations for implementing an ETL process, and then focuses on Microsoft SQL Server Integration Services (SSIS) as a platform for building ETL solutions.

 

Lessons

Introduction to ETL with SSIS

Exploring Data Sources

Implementing Data Flow

 

Lab: Implementing Data Flow in an SSIS Package

 

After completing this module, you will be able to:

Describe the key features of SSIS.

Explore source data for an ETL solution.

Implement a data flow by using SSIS

 

Module 5: Implementing Control Flow in an SSIS Package

This module describes how to implement ETL solutions that combine multiple tasks and workflow logic.

 

Lessons

Introduction to Control Flow

Creating Dynamic Packages

Using Containers

Managing Consistency

 

Lab: Implementing Control Flow in an SSIS Package

Lab: Using Transactions and Checkpoints

 

After completing this module, you will be able to:

Implement control flow with tasks and precedence constraints

Create dynamic packages that include variables and parameters

Use containers in a package control flow

Enforce consistency with transactions and checkpoints

 

Module 6: Debugging and Troubleshooting SSIS Packages

This module describes how you can debug packages to find the cause of errors that occur during execution. It then discusses the logging functionality built into SSIS that you can use to log events for troubleshooting purposes. Finally, the module describes common approaches for handling errors in control flow and data flow.

 

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

Implement logging for an SSIS package

Handle errors in an SSIS package

 

Module 7: Implementing an Incremental ETL Process

This module describes the techniques you can use to implement an incremental data warehouse refresh process.

 

Lessons

Introduction to Incremental ETL

Extracting Modified Data

Loading Modified data

 

Lab: Extracting Modified Data

Lab: Loading Incremental Changes

 

After completing this module, you will be able to:

Plan data extraction

Extract modified data

 

Module 8: Enforcing Data Quality

This module introduces Microsoft SQL Server Data Quality Services (DQS), and describes how you can use it to cleanse and deduplicate data.

 

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 how Data Quality Services can help you manage data quality

Use Data Quality Services to cleanse your data

Use Data Quality Services to match data

 

Module 9: Using Master Data Services

Master Data Services provides a way for organizations to standardize data and improve the quality, consistency, and reliability of the data that guides key business decisions. This module introduces Master Data Services and explains the benefits of using it.

 

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 key Master Data Services concepts

Implement a Master Data Services model

Use Master Data Services tools to manage master data

Use Master Data Services tools to create a master data hub

 

Module 10: Extending SQL Server Integration Services

This module describes the techniques you can use to extend SSIS. The module is not designed to be a comprehensive guide to developing custom SSIS solutions, but to provide an awareness of the fundamental steps required to use custom components and scripts in an ETL process that is based on SSIS.

 

Lessons

Using Scripts in SSIS

Using Custom Components in SSIS

 

Lab: Using Custom Components and Scripts

 

After completing this module, you will be able to:

Include custom scripts in an SSIS package

Describe how custom components can be used to extend SSIS

 

Module 11: Deploying and Configuring SSIS Packages

In this module, students will learn how to deploy packages and their dependencies to a server, and how to manage and monitor the execution of deployed 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 considerations for SSIS deployment.

Deploy SSIS projects.

Plan SSIS package execution.

 

Module 12: Consuming Data in a Data Warehouse

This module introduces business intelligence (BI) solutions and describes how you can use a data warehouse as the basis for enterprise and self-service BI.

 

Lessons

Introduction to Business Intelligence

Introduction to Reporting

An Introduction to Data Analysis

 

Lab: Using Business Intelligence Tools

 

After completing this module, you will be able to:

Describe BI and common BI scenarios

Describe how a data warehouse can be used in enterprise BI scenarios

Describe how a data warehouse can be used in self-service BI scenarios

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