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.


Objectivos

• 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.






Duração: 30 horas


Conteúdo Programático

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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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


Requisitos

• Designing a normalized database.
• Creating tables and relationships.
• Querying with Transact-SQL.
• Some exposure to basic programming constructs (such as looping and branching).


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