Data modeling is the process of creating a visual blueprint of your business data to structure how it is collected, stored, and related. It translates real-world business rules into organized technical schemas, ensuring consistency, scalability, and efficiency in databases and data warehouses. [1, 2]
The 3 Levels of Data Modeling
Data models progress from abstract business ideas to concrete technical blueprints.
• Conceptual Data Model: The highest level. It defines what data is needed (e.g., customers, products, orders) and general business rules. It acts as a shared language between technical teams and business stakeholders.
• Logical Data Model: The middle layer. It outlines detailed data structures, attributes, and exact relationships. It is independent of any specific database management system.
• Physical Data Model: The technical implementation layer. It details how data will be physically stored in a specific system (e.g., SQL Server, Oracle, data lakehouse), including data types, indexes, and partitions. [1, 2]
Core Modeling Components
Regardless of the model, these are the fundamental building blocks:
• Entities: The "things" or concepts you want to track (e.g., Customer, Employee, Product). These typically become tables in a database.
• Attributes: The specific characteristics of an entity. For example, a Customer entity might have attributes like Name, Email, and Phone Number.
• Relationships: How entities interact with each other. For example, a Customer "places" an Order.
• Cardinality: Defines the numerical relationship between entities (e.g., One-to-One, One-to-Many, or Many-to-Many).
• Primary & Foreign Keys: Unique identifiers. A Primary Key uniquely identifies a specific record (like a Customer ID), while a Foreign Key is an attribute that links back to the primary key in another table, establishing a relationship. [1, 11, 12, 13, 14]
Key Methodologies
Depending on whether you are building a transactional application or an analytical dashboard, you'll use different modeling styles:
• Entity-Relationship (ER) Modeling: Used primarily for Operational/Transactional systems (OLTP). It focuses on reducing data redundancy through a process called normalization, ensuring every piece of data is stored in exactly one place.
• Dimensional Modeling: Used for Data Warehouses and Analytics (OLAP). It organizes data into Facts (quantitative events like sales transactions) and Dimensions (descriptive contexts like store locations or dates). [2]
Best Practices
• Understand the Business Purpose: Technical design must always serve business needs; knowing exactly what metrics the business wants to track dictates the model's structure.
• Avoid Fact-to-Fact Joins: In dimensional modeling, joining two fact tables directly often indicates an error in the model.
• Use Surrogate Keys: When building data warehouses, professionals on Reddit generally agree that using artificial, integer-based keys (surrogate keys) simplifies joining tables and managing historical data. [19, 20, 21]
AI can make mistakes, so double-check responses
[1] https://www.databricks.com/blog/what-is-data-modeling
[2] https://www.sap.com/resources/what-is-data-modeling
[3] https://www.mongodb.com/resources/basics/databases/data-modeling
[4] https://www.geeksforgeeks.org/data-analysis/data-modeling-a-comprehensive-guide-for-analysts/
[5] https://www.scribd.com/document/610970256/DATA-MODELLING
[6] https://learning.sap.com/courses/becoming-an-sap-data-architect/transforming-business-concepts-with-data-modeling
[7] https://community.sap.com/t5/technology-q-a/conceptual-logical-physical-modeling/qaq-p/11584240
[8] https://agiledata.org/essays/datamodeling101.html
[9] https://atlan.com/what-is/data-modeling-concepts/
[10] https://www.quest.com/learn/conceptual.aspx
[11] https://medium.com/business-architected/conceptual-data-modelling-start-with-business-use-cases-10b3f2670d47
[12] https://www.datamation.com/big-data/types-of-data-modeling/
[13] https://www.workday.com/en-us/perspectives/ai/intro-to-data-modeling.html
[14] https://jcsites.juniata.edu/faculty/rhodes/dbms/ermodel.htm
[15] https://www.packtpub.com/en-us/learning/how-to-tutorials/implementing-data-modeling-techniques-in-qlik-sense-tutorial
[16] https://www.sciencedirect.com/topics/computer-science/normalized-model
[17] https://atlan.com/what-is-data-modeling/
[18] https://www.red-gate.com/blog/database-design-patterns/
[19] https://www.reddit.com/r/dataengineering/comments/1onxcfo/data_modeling_what_is_the_most_important_concept/
[20] https://www.reddit.com/r/dataengineering/comments/1onxcfo/data_modeling_what_is_the_most_important_concept/
[21] https://www.reddit.com/r/dataengineering/comments/1onxcfo/data_modeling_what_is_the_most_important_concept/
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