Implementing a Data Warehouse with Microsoft SQL Server (40 Hrs.)

Implementing a Data Warehouse with Microsoft SQL Server (40 Hrs.)

$200

At course completion

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 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.

Description

Implementing a Data Warehouse with Microsoft SQL Server (40 Hrs.) 2

Implementing a Data Warehouse with Microsoft SQL Server

About this course
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®, 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 . It covers the new features in SQL Server, but also the important capabilities across the SQL Server data platform.

Audience profile
This course is intended for database professionals who need to fulfil 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 SSIS packages for data extraction, transformation, and loading.
Enforcing data integrity by using Master Data Services.
Cleansing data by using Data Quality Services.

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