Course Syllabus

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Note: This course code has changed from CIT 327 to ITM 327. You may occasionally see references to the old course code or title.

Course Description

This course defines the theory and practice of how database warehouse systems are designed and managed. Students will learn how to distinguish between Kimball and Inmon methods, data marts and warehouses, and Extract, Transform, and Load (ETL) processes from relational column, document store, and XML databases and physical files.

Course Outcomes

  • Understand Kimball and Inmon data mart and warehousing design methods.
  • Understand the solution space of relational database, NoSQL (Not only SQL) database, XML database, and file management systems.
  • Understand the nature and use of the Kimball method, including how to write star queries and use basic SQL statistical functions.
  • Understand how to set the granularity of a Kimball star or snowflake schema from disparate sources using interpolation and extrapolation of data through the Extract, Transform, and Load (ETL) process.
  • Understand how to translate Online Transactional Processing(OLTP) repositories using Extract, Transform, and Load (ETL) methodologies that implement the Kimball method and what Online Analytical Processing (OLAP) is.
  • Understand the telemetry and technical debt of IT solutions.

Prerequisites

Complete ONE of the following:

  • ITM 111 - Introduction to Databases 
  • ITM 225 - Database Design and Development 

Course Details

Overview

How does this course relate to other courses?

This course fits between the ITM 111 (Introduction to Databases) or ITM 225 (Database Design and Development) and ITM 381 (Business Intel and Analytics). It builds on the design concepts of Entity Relation Diagrams (ERDs) for Online Transaction Processing by introducing data marts and data warehouses.

What will I be prepared to do after this course?

The student will be prepared to assume a role working with data marts or data warehouses as a data analyst or business intelligence analyst intern. The student will also be prepared to take CIT 381 (Business Intel and Analytics).

Grading

  • Preparation (30%): Weekly preparation quizzes consisting of a study materials quiz and a textbook chapter reading quiz.
  • Teach One Another (30%): Weekly group presentations which prepare students prepare to complete their weekly papers.
  • Ponder and Prove (40%): Weekly papers critiquing articles, presentations, and posts that are instrumental in exposing the student to data warehousing and a semester paper that requires each student to summarize their individual learning in a capstone case study.

Groups

Students will be expected to work together in groups regularly in this course. The course is set up to have one set of groups week 3-6, a separate group week 7-10, and a separate group week 11-12. Students will sign up in groups of two or three for each assignment.  The assignments are designed to give you an opportunity to collaborate with others and formulate the most important points from the study materials each week.

Technology and Textbooks

  • Designing Data-Intensive Applications by Martin Kleppmann,
    O'Reilly Media, Inc., 1st edition. 2017
    Print ISBN: 9781449373320
  • Linux Virtualization, Excel, Web Browser
  • Articles summarizing weekly materials
  • Articles and essays on the Internet

Semester Outline

Week 01 Learn the difference between a data mart and data warehouse.
Week 02 Learn the difference between the Kimball and Inmon methodologies.
Week 03 Learn how Extract, Transform, and Load (ETL) moves data from native data stores to a data mart or warehouse.
Week 04 Learn how granularity impacts Extract, Transform, and Load (ETL) processes.
Week 05 Learn how to draw and Entity Relationship Diagram for a star and snowflake schema that would support a data mart or warehouse.
Week 06 Learn how to analyze the telemetry and technical debt of IT.
Week 07 Learn how to analyze the telemetry and technical debt of vertical versus horizontal scaling of a massively concurrent application product.
Week 08 Learn how to analyze the telemetry and technical debt of using a document management database as a primary source for a Kimball data mart or warehouse.
Week 09 Learn the complexity of working with a document management database deployed in MongoDB.
Week 10 Learn how to use eXtensible Stylesheet Language Transformation (XSLT) to work with XML documents and access and transform traditional relational databases.
Week 11 Learn the issues linked to using XQuery to access XML documents in a MarkLogic XML database.
Week 12 Learn how to plan and schedule Extract, Transform, and Load (ETL) processes across diverse segments of source repositories into a Kimball data mart or warehouse.
Weeks 13 and 14 Capstone Case Study that individually assesses and presents a corporate assessment telemetry and technical debt that balances the needs of a document management system written in Node.js against a Kimball data mart or warehouse.

University Policies

Students with Disabilities

BYU-Pathway Worldwide is committed to providing a working and learning atmosphere that accommodates qualified students with disabilities. If you have a disability and require accommodations, please contact Accessibility. Reasonable academic accommodations are reviewed for all students who have qualified documented disabilities. Services are coordinated with students and instructors by Student Wellness.

This course may require synchronous meetings. If you are currently registered for accommodations and need a transcription for these meetings, please contact Accessibility.

Student Honor and Other Policies

Please click on the links below to learn more about the following policies:

Go to the Student Resources module for further resources and information.

Course Summary:

Date Details Due