Data Integration, Modeling, and ETL

A required course in the Business Intelligence & Data Warehousing Specialized Studies Program. An elective course in the Data Science, Database Management and Web Intelligence Certificate Programs.

This course provides both introductory and advanced concepts and techniques for developing effective dimensional models, data integration, and the ETL process. Learn how to build a high performance dimensional data model. A good dimensional model and its physical database form the hub of a business intelligence data warehouse, serving as the target of the data integration and as the source of business intelligence data. Learn how to design dimensional models for extensibility, employ a proven dimensional design process, apply the process to several representative situations, and understand a variety of advanced dimensional modeling techniques. The Extract, Transform, and Load (ETL) process is typically the most time-consuming, misunderstood, and underestimated task in building a data warehouse and other data integration applications. The ETL process addresses and resolves the challenges of extracting data from disparate operational source systems, storing it in the data staging area, profiling data for errors, cleaning and transforming the data, and mass loading it into the target enterprise data warehouse, data marts, or operational systems. Prerequisite: Experience using a relational database (as a minimum, Microsoft Access or similar product) is highly desirable.

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