Matakuliah Business Intelligence (Materi 4: Dimension Modelling)

Summary

This video introduces the concepts of dimensional and multidimensional modeling in business intelligence, focusing on their application in data warehouses. Dimensional modeling, developed by Ralph Kimball, involves organizing data into fact and dimension tables to optimize data retrieval. The video also explores key terms such as facts, dimensions, attributes, and schema types. Different data warehouse schema designs—Star, Snowflake, and Galaxy—are explained, each offering unique ways to organize and analyze data for efficient reporting and analysis. Overall, the session provides a comprehensive overview of structuring large datasets for fast and insightful analysis.

Takeaways

• 😀 Dimensional modeling is a data structure optimization technique used for data storage in data warehouses to enable faster data retrieval.
• 😀 The purpose of dimensional modeling is to optimize databases for efficient querying and retrieval of data in a data warehouse.
• 😀 Dimensional modeling consists of fact tables and dimension tables, which are key components of data warehouses.
• 😀 A 'fact' refers to measurements or metrics from business processes, such as sales numbers in a sales business process.
• 😀 'Dimensions' provide context around business events, answering questions like who, where, and what (e.g., customer, location, product).
• 😀 Attributes within dimensions describe characteristics, such as country, city, or postal code in a location dimension.
• 😀 Fact tables are the primary tables in dimensional modeling, containing measurements and foreign keys that link to dimension tables.
• 😀 Dimension tables are normalized and contain descriptive attributes about the facts, helping to filter and classify the data.
• 😀 Multidimensional modeling involves representing data in a cube, allowing for data analysis from multiple perspectives (e.g., product, time, customer).
• 😀 There are different types of multidimensional schemas used in data warehouses, such as Star schema, Snowflake schema, and Galaxy schema.
• 😀 The Star schema features a central fact table surrounded by dimension tables, making it simple and easy to understand.
• 😀 Snowflake schema involves more complex dimension tables with branching, which helps optimize storage space compared to Star schema.
• 😀 Galaxy schema, also known as a constellation schema, involves multiple fact tables sharing dimension tables, resembling a 'galaxy' structure.

Q & A

What is Dimensional Modeling in data warehousing?

Dimensional Modeling is a technique used for structuring data in a way that optimizes data storage and retrieval in data warehouses. The goal is to improve query performance and make it easier to analyze large amounts of data by organizing it into facts and dimensions.

Who developed the concept of Dimensional Modeling?

Dimensional Modeling was developed by Ralph Kimball, a prominent figure in the field of data warehousing and business intelligence.

What are facts in Dimensional Modeling?

Facts are measurements or metrics of business processes, typically numeric values like sales figures, quantities, or financial data. They represent the 'what' of the business process, such as the total sales per semester.

What are dimensions in Dimensional Modeling?

Dimensions provide context around the facts. They describe 'who,' 'where,' 'what,' and other descriptive aspects of a business process. For example, in a sales process, dimensions could include customer, location, and product.

What role do attributes play in Dimensional Modeling?

Attributes are characteristics of dimensions that provide further details for filtering or classifying facts. For example, a location dimension could have attributes like country, city, and postal code.

What is a fact table in Dimensional Modeling?

A fact table is the central table in a dimensional model, containing the numeric measures of the business process and foreign keys that link to dimension tables. It stores the 'facts' that are analyzed in business intelligence queries.

What is a dimension table in Dimensional Modeling?

A dimension table contains descriptive attributes related to the facts. It holds the detailed context (such as customer or location) used for analysis and is linked to the fact table via foreign keys.

What are the different types of Multidimensional Schemas?

There are three main types of multidimensional schemas: Star Schema, Snowflake Schema, and Galaxy Schema. Each schema organizes facts and dimensions in different ways to optimize query performance and storage.

What is a Star Schema?

A Star Schema is a simple multidimensional schema where a central fact table is surrounded by dimension tables. It is called a star schema because of its shape, resembling a star, and is known for its ease of understanding and performance efficiency.

What distinguishes a Snowflake Schema from a Star Schema?

A Snowflake Schema is more complex than a Star Schema because the dimension tables are normalized into multiple related tables, leading to a more compact storage structure. This can optimize storage but may reduce query performance compared to a Star Schema.

What is a Galaxy Schema?

A Galaxy Schema, also known as a Constellation Schema, consists of multiple fact tables that share dimension tables. This schema is used for more complex data analysis scenarios where multiple facts need to be analyzed using common dimensions.