Implementing a Data Warehouse with Microsoft SQL Server 2014

COURSE OUTLINE:

Description

In this course, you will learn how to implement a data warehouse platform to support a business intelligence (BI) solution. You will discover how to create a data warehouse, implement extract, transform, and load (ETL) with SQL Server Integration Services (SSIS), and validate and cleanse data with SQL Server Data Quality Services (DQS) and SQL Server Master Data Services.

This course is designed for customers interested in learning SQL Server 2012 or SQL Server 2014. It covers the new features of SQL Server 2014 as well as the important capabilities across the SQL Server data platform.

This course incorporates material from the Official Microsoft Learning Product 20463: Implementing a Data Warehouse with Microsoft SQL Server 2014. It covers the skills and knowledge measured by Exam 70-463 and along with on-the-job experience, helps you prepare for the exam.

Note: To reflect the inclusion of SQL Server 2014 content in exams 461, 462, and 463, Microsoft Learning has rebranded the MCSA certification to MCSA: SQL Server 2012/2014. As changes to each individual exam are modest in size and SQL Server 2012 skills remain relevant, all current MCSA: SQL Server 2012 certification holders will have their transcripts updated accordingly to reflect the new credential name. Please seehttps://www.microsoft.com/en-us/learning/mcsa-sql-certification.aspx for certification details.

Audience

  • Database professionals who need to fulfill a BI developer role focused on hands-on work, creating BI solutions included data warehouse implementation, ETL, and data cleansing
  • Database professionals responsible for implementing a data warehouse, developing SSIS packages for data extraction, loading, transferring, transforming, and enforcing data integrity using MDS, and cleansing data using DQS

Prerequisites

  • Minimum two years experience working with relational databases, including designing a normalized database, creating tables and relationships
  • Basic programming constructs, including looping and branching
  • Focus on key business priorities, such as revenue, profitability, and financial account

Learning Objectives

  • Data warehouse concepts and architecture considerations
  • Select an appropriate hardware platform for a data warehouse
  • Design and implement a data warehouse
  • Implement data flow and control flow in a SSIS package
  • Debug and troubleshoot SSIS packages
  • Implement an ETL solution that supports incremental data warehouse loads and extracting data
  • Implement data cleansing using Microsoft Data Quality Services(DQS)
  • Implement Master Data Services (MDS) to enforce data integrity
  • Extend SSIS with custom scripts and components
  • Deploy and configure SSIS packages
  • How Business Intelligence solutions consume data in a data warehouse

1. Data Warehousing

  • Concepts and Architecture Considerations
  • Considerations for a Data Warehouse Solution

2. Data Warehouse Infrastructure

  • Hardware Selections
  • Data Warehouse Reference Architectures and Appliances

3. Design and Implement a Data Warehouse

  • Logical Design,
  • Physical Implementation

4. Create an ETL Solution with SSIS

  • ETL with SSIS
  • Explore Source Data
  • Implement Data Flow

5. Implement Control Flow in an SSIS Package

  • Control Flow
  • Create Dynamic Packages
  • Using Containers
  • Manage Consistency

6. Debug and Troubleshoot SSIS Packages

  • Debug an SSIS Package
  • Log SSIS Package Events
  • Handle Errors in an SSIS Package

7. Implement an Incremental ETL Process

  • Incremental ETL

8. Enforce Data Quality

  • Microsoft SQL Server DQS
  • Use DQS to Cleanse Data
  • Use DQS to Match Data

9. Master Data Services

  • Master Data Services Concepts
  • Implement a Master Data Services Model
  • Manage Master Data and Create a Master Data Hub

10. Extend SQL Server Integration Services (SSIS)

  • Custom Components in SSIS
  • Scripting in SSIS

11. Deploy and Configure SSIS Packages

  • Deployment Considerations
  • Deploy SSIS Projects
  • Plan SSIS Package Execution

12. Consume Data in a Data Warehouse

  • Business Intelligence Solutions
  • Reporting and Data Analysis