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TETop 7 Data Modeling techniques for Structured and Unstructured Data 🔥
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1. Entity-Relationship (ER) Modeling
- Focuses on entities (objects or concepts) and their relationships.
- Used to design relational databases.
- Visualized using ER diagrams with entities, attributes, and relationships.
2. Hierarchical Data Modeling
- Organizes data in a tree-like structure with parent-child relationships.
- Used in XML and older databases like IBM's IMS.
- Best for data with a strict hierarchy.
3. Network Data Modeling
- A flexible approach where data is represented as nodes and relationships as edges.
- Useful for interconnected data like social networks or supply chains.
4. Relational Data Modeling
- Organizes data into tables (relations) with rows and columns.
- Supports normalization to reduce data redundancy.
- Popular in SQL-based databases like MySQL, PostgreSQL, and Oracle.
5. Dimensional Data Modeling
- Used in data warehouses and business intelligence.
- Employs fact and dimension tables to support analytical queries.
- Example: Star schema and Snowflake schema.
6. Object-Oriented Data Modeling
- Combines data and its behavior (methods) in objects.
- Suitable for complex data structures and modern applications.
- Compatible with object-oriented programming languages.
7. NoSQL Data Modeling
- Tailored for non-relational databases like MongoDB, Cassandra, or DynamoDB.
- Handles unstructured, semi-structured, or rapidly changing data.
- Examples: Document, Key-Value, Graph, and Column-family models.
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Which one are you?
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