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Schema Refinement and Normal Forms Chapter 19Database Management Systems, 3ed, R. Ramakrishnan and J. Gehrke1The Evils of Redundancy Redundancy is at the root of several problems associated with relational
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01
Identify the data fields that need to be included in the schema.
02
Determine the data types for each field (e.g. string, number, boolean).
03
Specify any constraints or validations for the data fields (e.g. required, length limit).
04
Define relationships between different data fields if necessary.
05
Document the schema using a standardized format (e.g. JSON Schema).
06
Test the schema by validating sample data against it.

Who needs schema refinement and normal?

01
Developers who are designing databases or data structures.
02
Data analysts who are working with large datasets.
03
Data engineers who are building data pipelines.
04
Data scientists who are performing analysis and modeling.
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Schema refinement refers to the process of improving and optimizing a data schema, making it more efficient and functional for data representation and retrieval. Normalization, on the other hand, is a specific technique used within schema refinement to organize the fields and tables of a database to minimize redundancy and dependency.
Individuals or businesses that manage databases or have data systems structured in a way that requires compliance with industry standards or regulations may be required to file schema refinement and normalization reports.
To fill out schema refinement and normalization, one should gather relevant data, analyze the existing schema, apply normalization rules (such as eliminating redundant data and establishing relationships between entities), and then document the updated schema structure and processes.
The purpose of schema refinement and normalization is to enhance data integrity, reduce data redundancy, and improve the efficiency of data operations within a database system.
Reported information typically includes details about the existing schema, the changes made during the refinement process, the rationale for these changes, and any new relationships or structures established within the data.
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