Design a star schema that contains facts and dimensions

Introduction:

The star schema is a popular data modeling technique used in business intelligence systems like Microsoft Power BI. It provides a simplistic yet efficient design for organizing data into a central structure that facilitates querying, analysis, and reporting. In this article, we will explore how to design a star schema specifically tailored to exams for the Microsoft Power BI Data Analyst certification, relying solely on the knowledge available in Microsoft documentation.

Understanding the Star Schema:

The star schema comprises a central fact table surrounded by multiple dimension tables. The fact table contains measurable and numerical data elements called facts, while dimension tables store descriptive attributes that provide context to the facts. This type of schema supports efficient querying and aggregation of data by avoiding complex joins typically associated with more normalized schemas.

Identifying Facts:

To design a star schema for the Power BI Data Analyst exams, we must first identify the relevant facts. Microsoft documentation provides insights into the key areas of focus for the exam. Facts for our star schema could include metrics such as the number of exam questions answered correctly, the time taken to complete the exam, the overall exam score, and the pass/fail status.

Defining Dimensions:

Dimensions provide additional context to the facts. For the Power BI Data Analyst exams, we can extract multiple dimensions from the Microsoft documentation. Some potential dimensions could include:

• Time Dimension: This dimension captures information related to the exam date, exam session, and time of completion. It allows for analysis of performance trends based on different time frames, such as monthly, quarterly, or yearly.
• Candidate Dimension: This dimension contains candidate-specific attributes such as candidate ID, name, location, and any relevant demographic details. It supports analysis of exam outcomes based on candidate profiles.
• Exam Module Dimension: As the exam covers various modules, this dimension captures information related to individual exam modules, such as module ID, name, and difficulty level. It facilitates analyzing exam performance based on different modules.
• Exam Question Dimension: This dimension stores information about each exam question, including question ID, type, topic, and difficulty level. It enables drilling down to question-specific performance analysis.

Designing the Star Schema:

Once we have identified the facts and dimensions, we can construct the star schema by creating the central fact table and connecting it to the associated dimension tables:

1. Fact Table: We create a fact table called “ExamResults” with columns representing the identified facts such as ExamID (foreign key), CandidateID (foreign key), CompletionTime, CorrectAnswers, Score, and Pass/Fail status.
2. Dimension Tables: We create individual tables for each dimension identified earlier, such as “Time,” “Candidate,” “ExamModule,” and “ExamQuestion.” These tables will include all relevant attributes specific to each dimension.

To establish relationships between the fact and dimension tables, we link the primary keys from the dimension tables to their corresponding foreign keys in the fact table. For instance, the ExamID column in the fact table will connect to the ExamID column in the ExamModule dimension table.

Conclusion:

Designing a star schema tailored to the Microsoft Power BI Data Analyst exams allows the efficient organization, analysis, and reporting of exam data. By leveraging the facts and dimensions specific to the exam from Microsoft documentation, we can create a comprehensive star schema. Remember, the dimensions should provide relevant context to the facts, enabling detailed analysis and accurate reporting for exam performance.