Implementing a Data Warehouse with Microsoft SQL Server 2012

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Implementing a Data Warehouse with Microsoft SQL Server 2012

Course duration: 5 days

Course Overview

Data warehousing is a solution organizations use to centralize business data for reporting and analysis. This five-day instructor-led course focuses on teaching individuals how to create a data warehouse with SQL Server 2012, implement ETL with SQL Server Integration Services, and validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services. This course helps people prepare for exam 70-463.

Audience Profile

This course is intended for database professionals who need to fulfill 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 SQL Server Integration Services (SSIS) packages for data extraction, transformation, and loading (ETL).
  • Enforcing data integrity by using Master Data Services.
  • Cleansing data by using Data Quality Services.Before attending this course, student should have:

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 starting a data warehousing project.

Lessons

  • Overview of Data Warehousing
  • Considerations for a Data Warehouse Solution
  • Lab: Exploring a Data Warehousing Solution
  • Exploring data sources
  • Exploring an ETL solution
  • Exploring a data warehouse
  • After completing this module, students will be able to:
  • Describe the key elements of a data warehousing solution.
  • Describe the key considerations for a data warehousing project.This module describes the characteristics of typical data warehouse workloads, and explains how you can use reference architectures and data warehouse appliances to ensure you build the system that is right for your organization.

Lessons

Module 2: Data Warehouse Hardware

  • Considerations for Building a Data Warehouse
  • Data Warehouse Reference Architectures and Appliances
  • After completing this module, students 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.In this module, you will learn how to implement the logical and physical architecture of a data warehouse based on industry-proven design principles.

Lessons

Module 3: Designing and Implementing a Data Warehouse

  • Logical Design for a Data Warehouse
  • Physical Design for a Data Warehouse
  • Lab: Implementing a Data Warehouse Schema
  • Implementing a Star Schema
  • Implementing a Snowflake Schema
  • Implementing a Time Dimension Table
  • After completing this module, students will be able to:
  • Implement a logical design for a data warehouse.
  • Implement a physical design for a data warehouse.This module discusses considerations for implementing an ETL process, and then focuses on SQL Server Integration Services (SSIS) as a platform for building ETL solutions.

Lessons

Module 4: Creating an ETL Solution with SSIS

  • Introduction to ETL with SSIS
  • Exploring Source Data
  • Implementing Data Flow
  • Lab: Implementing Data Flow in a SSIS Package
  • Exploring Source Data
  • Transferring Data by Using a Data Flow Task
  • Using Transformations in a Data Flow
  • After completing this module, students will be able to:
  • Describe the key features of SSIS.
  • Explore source data for an ETL solution.
  • Implement a data flow using SSIS.Control flow in SQL Server Integration Services packages enables you to implement complex ETL solutions that combine multiple tasks and workflow logic. This module covers how to implement control flow, and design robust ETL processes for a data warehousing solution that coordinate data flow operations with other automated tasks.

Lessons

Module 5: Implementing Control Flow in an SSIS Package

  • Introduction to Control Flow
  • Creating Dynamic Packages
  • Using Containers
  • Managing Consistency
  • Lab: Implementing Control Flow in an SSIS Package
  • Using Tasks and Precedence in a Control Flow
  • Using Variables and Parameters
  • Using Containers
  • Lab: Using Transactions and Checkpoints
  • Using Transactions
  • Using Checkpoints
  • After completing this module, students 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 SQL Server Integration Services (SSIS) packages to find the cause of errors that occur during execution. Then module then covers the logging functionality built into SSIS 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

Module 6: Debugging and Troubleshooting SSIS Packages

  • Debugging an SSIS Package
  • Logging SSIS Package Events
  • Handling Errors in an SSIS Package
  • Lab: Debugging and Troubleshooting 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, students 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.

Lessons

Module 7: Implementing an Incremental ETL Process

  • Introduction to Incremental ETL
  • Extracting Modified Data
  • Loading Modified Data
  • Lab: Extracting Modified Data
  • Using a DateTime Column to Incrementally Extract Data
  • Using a Change Data Capture
  • Using Change Tracking
  • Lab: Loading Incremental Changes
  • Using a Lookup Transformation to Insert Dimension Data
  • Using a Lookup Transformation to Insert or Update Dimension Data
  • Implementing a Slowly Changing Dimension
  • Using a MERGE Statement to Load Fact Data
  • After completing this module, students will be able to:
  • Describe the considerations for implementing an incremental extract, transform, and load (ETL) solution.
  • Use multiple techniques to extract new and modified data from source systems.
  • Use multiple techniques to insert new and modified data into a data warehouse.In this module, you will learn about how you can use cloud computing in your data warehouse infrastructure and learn about the tools and services available from the Microsoft Azure Marketplace.

Lessons

Module 8: Incorporating Data from the Cloud into a Data Warehouse

  • Overview of Cloud Data Sources
  • SQL Server Database
  • The Windows Azure Marketplace
  • Lab: Using Cloud Data in a Data Warehouse Solution
  • Creating a SQL Azure Database
  • Extracting Data from a SQL Azure Database
  • Obtaining Data from the Windows Azure Marketplace
  • After completing this module, students will be able to:
  • Describe cloud data scenarios.
  • Describe SQL Azure.
  • Describe the Windows Azure Marketplace.Ensuring the high quality of data is essential if the results of data analysis are to be trusted. This module explains how to use the SQL Server 2012 Data Quality Services (DQS) to provide a computer assisted process for cleansing data values and identifying and removing duplicate data entities.

Lessons

Module 9: Enforcing Data Quality

  • Introduction to Data Quality
  • Using Data Quality Services to Cleanse Data
  • Using Data Quality Services to Match Data
  • Lab: Cleansing Data
  • Creating a DQS Knowledge Base
  • Using a DQS Project to Cleanse Data
  • Using DQS in an SSIS Package
  • Lab: Deduplicating Data
  • Creating a Matching Policy
  • Using a DQS Project to Match Data
  • After completing this module, students 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.This module introduces Master Data Services and explains the benefits of using it in a data warehousing context. The module also describes the key configuration options for Master Data Services, and explains how to import and export data. Finally, the module explains how to apply rules that help to preserve data integrity, and introduces the new Master Data Services Add-in for Excel.

Lessons

Module 10: Using Master Data Services

  • Introduction to Master Data Services
  • Implementing a Master Data Services Model
  • Using the Master Data Services Add-in for Excel
  • Lab: Implementing Master Data Services
  • Creating a Basic Model
  • Editing a Model by Using the Master Data Services Add-in for Excel
  • Loading Data into a Model
  • Enforcing Business Rules
  • Consuming Master Data Services Data
  • After completing this module, students will be able to:
  • Describe key Master Data Services concepts.
  • Implement a Master Data Services model.
  • Use the Master Data Services Add-in for Excel to view and modify a model.This module describes the techniques you can use to extend SQL Server Integration Services (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

Module 11: Extending SQL Server Integration Services

  • Using Custom Components in SSIS
  • Using Scripts in SSIS
  • Lab: Using Custom Components and Scripts
  • Using a Custom Component
  • Using a Script Task
  • After completing this module, students will be able to:
  • Describe how custom components can be used to extend SSIS.
  • Describe how you can include custom scripts in an SSIS package.SQL Server Integration Services provides tools that make it easy to deploy packages to another computer. The deployment tools also manage any dependencies, such as configurations and files that the package needs. In this module, you will learn how to use these tools to install packages and their dependencies on a target computer.

Lessons

Module 12: Deploying and Configuring SSIS Packages

  • Overview of SSIS Deployment
  • Deploying SSIS Projects
  • Planning SSIS Package Execution
  • Lab: Deploying and Configuring SSIS Packages
  • Create a SSIS Catalog
  • Deploy an SSIS Project
  • Create Environments for an SSIS Solution
  • Running an SSIS Package in SQL Server Management Studio
  • Scheduling SSIS Packages with SQL Server Agent
  • After completing this module, students will be able to:
  • Describe SSIS deployment.
  • Explain how to deploy SSIS projects using the project deployment model.
  • Plan SSIS package execution.This module introduces Business Intelligence (BI), describes the components of SQL Server that you can use to create a BI solution, and the client tools that users can use to create reports and analyze data.

Lessons

Module 13: Consuming Data in a Data Warehouse

  • Introduction to Business Intelligence
  • Introduction to Reporting
  • Introduction to Data Analysis
  • Lab: Using Business Intelligence Tools
  • Exploring a Reporting Services Report
  • Exploring a PowerPivot Workbook
  • Exploring a Power View Report
  • After completing this module, students will be able to:
  • Describe BI and common BI scenarios.
  • Explain the key features of SQL Server Reporting Services.
  • Explain the key features of SQL Server Analysis Services.