The document discusses various data modeling techniques essential for 2024, including UML modeling, dimensional designs, and NoSQL approaches. It highlights the importance of optimizing for performance, scalability, and data integrity using different modeling strategies such as star schema and data vault. The article emphasizes the need for understanding business data requirements and adapting to complex relationships in modern data environments.

1. DATA
MODELIN
G
TECHNIQUES
YOU NEED
TO ADOPT IN
2024

2. Data
Modeling
Technique
what why who
UML Modeling Standard Capture and Software
language for communicate
system
engineers,
designers
Dimensional Designing
designs
Optimized for Data
Modeling data query architects,
warehouses performance business
analysts
Star Schema Schema with Simplifies Data
Modeling fact tables queries and
aggregations
warehouse
designers,
analysts
Physical Data Designing Optimizes for Database
Modeling database storage and administrator
schema performance s, developers
Logical Data Representing Ensures data Data
Modeling data integrity and architects,
structure consistency database
designers

3. Data
Modeling
what why who
Technique
Entity- Representing Model real- Database
Relationship relationships world entities designers,
Modeling analysts
Relational Modeling Organizes Database
Modeling data using data in a designers,
tables structured
manner
developers
Object- Combining Handles Database
Relational OOP and complex data designers,
Modeling relational types and
relationships
developers
Object- Storing data Mimics real- Developers,
Oriented as objects world objects architects
Database
Model
Conceptual High-level Understand Business
Data modeling business analysts,
Modeling requirements stakeholders

4. Data
Modeling
Technique
what why who
Graph data Data Suitable for Data scientists,
model represented complex graph database
as network relationships developers
Network Hierarchical Efficient for Database
Technique data model complex
relationships
designers
Hierarchical Data Simple and Database
Technique organized in
a tree-like
easy to
understand
designers
NoSQL Data Designing Scalability, Developers,
Modeling databases flexibility,
performance
architects
Temporal Modeling Captures Data architects,
Data data with data changes analysts
Modeling time over time

5. Data
Modeling
Technique
what why who
Fact-Based Modelling Clear Data architects,
Modeling facts, representation of business
dimensions business processes analysts
Data Vault Optimized for Scalability and Data architects,
Modeling data
warehousing
flexibility ETL developers
Key-Value Data stored Simple and Developers,
Data as key-value flexible structure architects
Modeling pairs
Document Organizing Flexible schema, Developers,
Data data as efficient for architects
Modeling documents hierarchy
Snowflake Extension of Reduces data Data
Schema star schema redundancy,
improves
warehouse
designers
performance

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