Implementing a Data Warehouse with Microsoft SQL Server 2012/2014

Learn how to implement a data warehouse platform to support a BI solution

Course Code : 5040

Overview

This five day course is intended for database professionals who need to fulfill a business intelligence developer role. The course helps participants learn how to go about creating BI solutions including data warehouse implementation, ETL, and data cleansing. The course covers 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. The course is a good fit for participants who are interested in learning SQL Server 2012 as well as SQL Server 2014.

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Course Delivery

This course is available in the following formats:

Live Classroom
Duration: 5 days

Live Virtual Classroom
Duration: 5 days

What You'll learn

  • 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

Outline

  • Overview of Data Warehousing
  • Considerations for a Data Warehouse Solution
  • Lab: Exploring a Data Warehousing Solution
  • Considerations for building a Data Warehouse
  • Data Warehouse Reference Architectures and Appliances
  • Lab: Planning Data Warehouse Infrastructure
  • Logical Design for a Data Warehouse
  • Physical design for a data warehouse
  • Lab: Implementing a Data Warehouse Schema
  • Introduction to ETL with SSIS
  • Exploring Data Sources
  • Implementing Data Flow
  • Lab: Implementing Data Flow in an SSIS Package
  • Introduction to Control Flow
  • Creating Dynamic Packages
  • Using Containers
  • Managing Consistency
  • Lab: Implementing Control Flow in an SSIS Package
  • Lab: Using Transactions and Checkpoints
  • Debugging an SSIS Package
  • Logging SSIS Package Events
  • Handling Errors in an SSIS Package
  • Lab: Debugging and Troubleshooting an SSIS Package
  • Planning Data Extraction
  • Extracting Modified Data
  • Lab: Extracting Modified Data
  • Planning Data Loads
  • Using SSIS for Incremental Loads
  • Using Transact-SQL Loading Techniques
  • Lab: Loading a Data Warehouse
  • 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
  • Introduction to Master Data Services
  • Implementing a Master Data Services Model
  • Managing Master Data
  • Creating a Master Data Hub
  • Lab: Implementing Master Data Services
  • Using Scripts in SSIS
  • Using Custom Components in SSIS
  • Lab: Using Custom Scripts
  • Overview of SSIS Deployment
  • Deploying SSIS Projects
  • Planning SSIS Package Execution
  • Lab: Deploying and Configuring SSIS Packages
  • Introduction to Business Intelligence
  • Enterprise Business Intelligence
  • Self-Service BI and Big Data
  • Lab: Using a data warehouse
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Prerequisites

Participants need to have at least two years of experience working with relational databases, including, designing a normalized database, creating tables and relationships, querying with Transact-SQL and some exposure to basic programming contructs, such as, looping and branching. A basic understanding of key business priorities, such as, revenue, profitability and financial accounting is recommended.

Who Should Attend

The course is highly recommended for –

  • SQL Business Intelligence developers
  • Data warehouse administrators
  • System analysts
  • SQL developers
  • Business analysts
  • Data modelers
  • Big Data developers

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