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FS 2015 Data Modelling and Databases Exercise Sheet 6 Discussion: March 31 / April 3 ETH Zurich Systems Group Prof. Gustavo Alonso Normal Forms I 1 Functional Dependencies and Keys (a) Consider the

How to fill out data modelling and databases

How to fill out data modelling and databases:

1. Understand the purpose: Before filling out data modelling and databases, it is important to have a clear understanding of the purpose. Determine what specific data needs to be stored and how it will be used.
2. Identify entities and attributes: Start by identifying the entities, which are the main objects or concepts in the data model. Then, define the attributes for each entity, which are the characteristics or properties of the entities.
3. Establish relationships: Determine the relationships between the different entities in the data model. This helps to define how they are connected and how they interact with each other. Use appropriate relationship types such as one-to-one, one-to-many, or many-to-many.
4. Define data constraints: Specify any constraints or rules that need to be enforced on the data model. This can include things like data types, maximum or minimum values, uniqueness constraints, or referential integrity constraints.
5. Normalize the data: Normalize the data model to eliminate redundancy and improve data integrity. This involves breaking down the data into smaller and more manageable tables and ensuring that each table represents a single subject.
6. Document the data model: Document the data model by creating a clear and detailed documentation that describes the entities, attributes, relationships, and constraints. This documentation serves as a reference for developers, analysts, and other stakeholders who need to understand the data model.

Who needs data modelling and databases:

1. Businesses and organizations: Data modelling and databases are essential for businesses and organizations that need to store and manage large amounts of data. This includes companies in various industries such as finance, healthcare, retail, and manufacturing.
2. Data analysts and database administrators: Data analysts and database administrators are directly involved in data modelling and databases. They need data models to analyze and interpret data, and they manage and maintain databases to ensure data integrity and security.
3. Software developers: Software developers need data modelling and databases to design, develop, and implement applications that require data storage and retrieval. They use data models as a blueprint for creating database structures and integrating them into their software systems.
4. Researchers and academics: Researchers and academics often work with large datasets and need data modelling and databases to organize and analyze their data. They use data models to design experiments, store research findings, and perform statistical analysis.
5. Government agencies: Government agencies collect and process vast amounts of data for various purposes, such as census data, crime statistics, and economic indicators. Data modelling and databases are crucial for efficiently managing and analyzing this data.

In summary, filling out data modelling and databases involves understanding the purpose, identifying entities and attributes, establishing relationships, defining data constraints, normalizing the data, and documenting the data model. The need for data modelling and databases extends to businesses, data analysts, software developers, researchers, academics, and government agencies.

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