Implementing a Data Warehouse with Microsoft SQL Server 2012 Boot Camp

Description

This 5-day instructor-led course describes how to implement a BI platform to support information worker analytics. Students will learn 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 the exam 70-463.

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.

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 SSIS solution that supports incremental data warehouse loads and changing data.
• Integrate cloud data into a data warehouse ecosystem infrastructure.
• 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 information workers can consume data from the data warehouse.

Microsoft Course 10777A

Prerequisites

Before attending this course, student should have:

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.

What’s included?

  • Authorized Courseware
  • Intensive Hands on Skills Development with an Experienced Subject Matter Expert
  • Hands on practice on real Servers and extended lab support 1.800.482.3172
  • Examination Vouchers & Onsite Certification Testing- (excluding Adobe and PMP Boot Camps)
  • Academy Code of Honor: Test Pass Guarantee
  • Optional: Package for Hotel Accommodations, Lunch and Transportation

With several convenient training delivery methods offered, The Academy makes getting the training you need easy. Whether you prefer to learn in a classroom or an online live learning virtual environment, training videos hosted online, and private group classes hosted at your site. We offer expert instruction to individuals, government agencies, non-profits, and corporations. Our live classes, on-sites, and online training videos all feature certified instructors who teach a detailed curriculum and share their expertise and insights with trainees. No matter how you prefer to receive the training, you can count on The Academy for an engaging and effective learning experience.

Methods

  • Instructor Led (the best training format we offer)
  • Live Online Classroom – Online Instructor Led
  • Self-Paced Video

Speak to an Admissions Representative for complete details

Curriculum

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 embarking on a data warehousing project.

Lessons

Describe data warehouse concepts and architecture considerations
Considerations for a Data Warehouse SolutionLab : Exploring a Data Warehousing Solution
Exploring Data Sources•Exploring an ETL Process•Exploring a Data WarehouseDescribe data warehouse concepts and architecture considerations.

Module 2: Data Warehouse Hardware Considerations
This module describes the considerations for selecting the appropriate hardware platform for your data warehouse solution.

Lessons

The Challenges of Building a Data Warehouse
Data Warehouse Reference Architectures
Data Warehouse Appliances
Select an appropriate hardware platform for a data warehouse.

Module 3: Designing and Implementing a Data Warehouse
This module describes how to implement the logical and physical architecture of a data warehouse based on industry proven design principles.

Lessons

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
Implement a Time Dimension Table
Design and implement a schema for a data warehouse.

Module 4: Design and implement a schema 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

Introduction to ETL with SSIS
Exploring Source Data
Implementing Data Flow

Lab : Implementing Data Flow in an SSIS Package

Exploring Source Data
Transfer Data with a Data Flow Task
Using Transformations in a Data Flow
Implement Data Flow in an SSIS Package

Module 5: Implementing Control Flow in an SSIS Package
This module describes how to implement control flow which allows users to design robust ETL processes for a data warehousing solution that coordinate data flow operations with other automated tasks.

Lessons

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 CheckpointsImplement control flow in an SSIS package.

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

Debugging an SSIS Package
Logging SSIS Package Execution
Implementing an Event Handler
Handling Errors in a Data FlowDebug and Troubleshoot SSIS packages.

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

Using a DateTime Column to Incrementally Extract Data
Using a DateTime Column to Incrementally Extract Data
Using Change Tracking

Lab : Loading Incremental Changes

Using a Lookup task to insert dimension data
Using a Lookup task to insert or update dimension data
Implementing a Slowly Changing Dimension
Using a MERGE statement to load fact dataImplement an SSIS solution that supports incremental DW loads and changing data.

Module 8: Incorporating Data from the Cloud in a Data WarehouseThis modules describes how integrate cloud data into a data warehouse ecosystem.

Lessons

Overview of Cloud Data Sources
SQL Server Azure
Azure Data Market

Lab : Using Cloud data in a Data Warehouse Solution

Extracting data from SQL Azure
Acquiring Data from the Azure Data Market
Integrate cloud data into a data warehouse ecosystem.

Module 9: Enforcing Data QualityThis modules describes how to use Data Quality Services (DQS) for cleansing and deduplicating your data.

Lessons

Introduction to Data Cleansing
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
Use DQS in an SSIS Package

Lab : De-Duplicating Data

Creating a Matching Policy
Using a DQS Project to Match DataImplement data cleansing by using Microsoft Data Quality Services.

Module 10: Using Master Data Services
This module introduces Master Data Services and explains the benefits of using it in a business intelligence (BI) context. It also describes the key configuration options, explains how to import and export data and apply rules that help to preserve data integrity, and introduces the new Master Data Services Add-in for Excel.

Lessons

Master Data Services Concepts
Implementing a Master Data Services Model
Using the Master Data Services Excel Add-in

Lab : Implementing Master Data Services

Creating a Basic MDS Model
Editing an MDS Model With Excel
Loading Data into MDS
Enforcing Business Rules
Consuming Master Data Services Data
Implement Master Data Services to enforce data integrity at source.

Module 11: Extending SSIS
This module describes how to extend SSIS by using custom scripts and components.

Lessons

Using Custom Components in SSIS
Using Scripting in SSIS

Lab : Using Scripts and Custom Components

Using a Custom Component
Using the Script TaskExtend SSIS with custom scripts and components

Module 12: Deploying and Configuring SSIS PackagesThis modules describes how to deploy and configure SSIS packages.

Lessons

Overview of Deployment
Deploying SSIS Projects
Planning SSIS Package Execution

Lab : Deploying and Configuring SSIS Packages

Create an 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 AgentDeploy and configure SSIS packages.

Module 13: Consuming Data in a Data Warehouse
This module describes how information workers can consume data from the data warehouse.

Lessons

Using Excel to Analyze Data in a data Warehouse.
An Introduction to PowerPivot
An Introduction to CrescentLab : Using a Data Warehouse
Use PowerPivot to Query the Data Warehouse
Visualizing Data by Using CrescentDescribe how information workers can consume data from the data warehouse.

Enrolled