Implementing a Data Warehouse with Microsoft® SQL Server®

Describe the key elements of a data warehousing solution

Describe the key considerations for a data warehousing project

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

Considerations for Data Warehouse Infrastructure

Planning Data Warehouse Hardware

Planning Data Warehouse Hardware

After completing this module, you will be able to:

Describe key considerations for BI infrastructure.

Plan data warehouse infrastructure.

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

Data Warehouse Design Overview

Designing Dimension Tables

Designing Fact Tables

Physical Design for a Data Warehouse

Implement a Star Schema

Implement a Snowflake Schema

Implement a Time Dimension

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

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.

Introduction to ETL with SSIS

Exploring Data Sources

Implementing Data Flow

Exploring Data Sources

Transferring Data by Using a Data Flow Task

Using Transformations in a Data Flow

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

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

Introduction to Control Flow

Creating Dynamic Packages

Using Containers

Managing Consistency

Using Tasks and Precedence in a Control Flow

Using Variables and Parameters

Using Containers

Using Transactions

Using 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

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.

Debugging an SSIS Package

Logging SSIS Package Events

Handling Errors in an SSIS Package

Debugging an SSIS Package

Logging SSIS Package Execution

Implementing an Event Handler

Handling Errors in a Data Flow

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

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

Planning Data Extraction

Extracting Modified Data

Using a Datetime Column

Using Change Data Capture

Using the CDC Control Task

Using Change Tracking

After completing this module, you will be able to:

Plan data extraction

Extract modified data

This module describes the techniques you can use to implement data warehouse load process.

Planning Data Loads

Using SSIS for Incremental Loads

Using Transact-SQL Loading Techniques

Loading Data from CDC Output Tables

Using a Lookup Transformation to Insert or Update Dimension Data

Implementing a Slowly Changing Dimension

Using the MERGE Statement

After completing this module, you will be able to:

Describe the considerations for planning data loads

Use SQL Server Integration Services (SSIS) to load new and modified data into a data warehouse

Use Transact-SQL techniques to load data into a data warehouse

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

Introduction to Data Quality

Using Data Quality Services to Cleanse Data

Using Data Quality Services to Cleanse Data

Creating a DQS Knowledge Base

Using a DQS Project to Cleanse Data

Using DQS in an SSIS Package

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

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.

Introduction to Master Data Services

Implementing a Master Data Services Model

Managing Master Data

Creating a Master Data Hub

Creating a Master Data Services Model

Using the Master Data Services Add-in for Excel

Enforcing Business Rules

Loading Data Into a Model

Consuming Master Data Services Data

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

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.

Using Scripts in SSIS

Using Custom Components in SSIS

Using a Script Task

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

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.

Overview of SSIS Deployment

Deploying SSIS Projects

Planning SSIS Package Execution

Creating an SSIS Catalog

Deploying an SSIS Project

Running an SSIS Package in SQL Server Management Studio

Scheduling SSIS Packages with SQL Server Agent

After completing this module, you will be able to:

Describe considerations for SSIS deployment.

Deploy SSIS projects.

Plan SSIS package execution.

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.

Introduction to Business Intelligence

Enterprise Business Intelligence

Self-Service BI and Big Data

Exploring an Enterprise BI Solution

Exploring a Self-Service BI Solution

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