Data Modeling

- Data modeling is a detailed process that involves creating a visual representation of data and its relationships. It serves as a blueprint for how data is structured, stored, and accessed to ensure consistency and clarity in data management

- A well-designed model helps prevent common issues like redundancy, performance bottlenecks, and difficulty adapting to future changes.  

Types of Data Models:
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- Conceptual Data Model: It defines key business entities and relationships without getting into technical details.

- Logic Data Model: It defines how the data is structured. It includes data's attributes, relationships, and constraints and also a bridge between business requirements and the physical implementation of the data. 

- Physical Data Model: It defines how the data is stored in a database like specific table structures, indexes, and storage mechanisms

Data Modeling Techniques:
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Data modeling is not a one-size-fits-all process. Different techniques are employed depending on the complexity of the data and the goals. There 4 different data modeling techniques

* Entity-relationship (ER) modeling
- Entities
- Relationships
- Attributes 

 * Dimensional modeling : Dimensional modeling is widely used in data warehousing and analytics, where data is often represented in terms of facts and dimensions.
- Fact table
- Dimension tables

* Object-oriented modeling: Object-oriented modeling is used to represent complex systems, where data and the functions that operate on it are encapsulated as objects.
- This approach is particularly beneficial in object-oriented programming (OOP) languages like Java and Python, where data models can be directly mapped to classes and objects.

* NoSQL and document-based modeling: NoSQL and document-based modeling techniques are designed for flexible, schema-less databases.
- In NoSQL databases like MongoDB, a document-based model organizes data into collections of documents, where each document can have a unique structure.
- This flexibility allows for faster iteration and scaling, particularly in big data environments or applications requiring high-speed data access.

Matching data models with data modeling techniques:
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* Conceptual data model → Entity-relationship (ER) modeling
* Logical data model → ER, dimensional, and object-oriented modeling
* Physical data model → Dimensional, object-oriented, and NoSQL modeling

Normalize early, denormalize when necessary
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- Normalization is a foundational concept in data modeling. It organizes data so that redundancy is minimized and consistency is ensured by structuring it into smaller, logically related tables.
- This process reduces the chances of anomalies and discrepancies, making the data easier to manage and update.

Future-proofing your data model :
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As businesses evolve, so do their data requirements. Designing a future-proof data model means creating one that’s flexible and scalable, ready to handle new data sources and changing demands. 

Imagine you’re designing a database for an online store. Initially, you track only domestic orders, but later, you expand globally. You'll need major modifications later if your schema only supports a single currency.

Instead of a simple Price column, include Currency_Code and Exchange_Rate fields.Use a flexible schema that allows new attributes without restructuring the database.

=================== Normalization in SQL (1NF - 5NF) ============================

Database normalization is an important process used to organize and structure relational databases. This process ensures that data is stored in a way that minimizes redundancy, simplifies querying, and improves data integrity.

In simpler terms, it involves breaking down a large, complex table into smaller and simpler tables while maintaining data relationships. Normalization is commonly used when dealing with large datasets. 

If a table is not properly normalized and has data redundancy, it will not only take up extra data storage space but also make it difficult to handle and update the database.

First Normal Form (1NF):