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How do you design SQL schemas that meet business requirements?

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Designing SQL schemas that meet business requirements is a key skill for database developers. SQL schemas define the structure, relationships, and constraints of the data stored in a database. A well-designed schema can improve the performance, security, and maintainability of your database, as well as make it easier to query and manipulate the data. In this article, you will learn how to design SQL schemas that meet business requirements by following these steps:

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1 Analyze the business problem

The first step is to understand the business problem that your database needs to solve. You should identify the goals, scope, and stakeholders of the project, as well as the data sources, processes, and rules that are involved. You should also gather the functional and non-functional requirements of the database, such as the expected volume, frequency, and complexity of the data, the security and privacy policies, and the performance and scalability expectations.

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Arunraj S.

AWS Certified Data Specialist | Data Migration, Data Reconciliation & Architecture Expert | 16+ Years Enhancing Data Integrity, System Migrations & AI Solutions | Skilled in MongoDB, Big Data, & Generative AI
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We also need to consider the RTO, RPO, backup frequencies etc here to chose the best database product for the required business use case. Considering the current cloud foot print and cross geographic businesses we should also consider the multi-region replication and availability use cases if applicable.

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2 Model the data

The next step is to model the data that your database will store and manipulate. You should use a conceptual data model, such as an entity-relationship diagram (ERD), to represent the entities, attributes, and relationships of the data in a graphical way. You should also use a logical data model, such as a relational schema, to define the tables, columns, keys, and constraints of the data in a more detailed way. You should check that your data model is consistent, complete, and normalized, meaning that it avoids redundancy and anomalies.

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Arunraj S.

AWS Certified Data Specialist | Data Migration, Data Reconciliation & Architecture Expert | 16+ Years Enhancing Data Integrity, System Migrations & AI Solutions | Skilled in MongoDB, Big Data, & Generative AI
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We need to consider the data normality requirement and design the model with specific normality standard for OLTP or OLAP application specifications.

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3 Choose the SQL dialect

The third step is to choose the SQL dialect that your database will use. SQL is a standard language for querying and manipulating relational databases, but different database systems may have different implementations and extensions of SQL. For example, MySQL, PostgreSQL, Oracle, and SQL Server are some of the popular SQL dialects that have different syntax, features, and limitations. You should choose the SQL dialect that best suits your business requirements, data model, and database system.

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Arunraj S.

AWS Certified Data Specialist | Data Migration, Data Reconciliation & Architecture Expert | 16+ Years Enhancing Data Integrity, System Migrations & AI Solutions | Skilled in MongoDB, Big Data, & Generative AI
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In my view, dialect is not too much different but need to understand the business use case feasibility and choose the database product accordingly. Developers and DBAs can learn and adopt to the dialect.

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4 Implement the schema

The fourth step is to implement the schema in your database system. You should use the data definition language (DDL) of your SQL dialect to create the tables, columns, keys, and constraints that you defined in your logical data model. You should also use the data manipulation language (DML) of your SQL dialect to insert, update, delete, and query the data in your tables. You should test and debug your schema and queries to ensure that they work as expected and meet the business requirements.

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Arunraj S.

AWS Certified Data Specialist | Data Migration, Data Reconciliation & Architecture Expert | 16+ Years Enhancing Data Integrity, System Migrations & AI Solutions | Skilled in MongoDB, Big Data, & Generative AI
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In my opinion, we also need to analyze and add the indexes on the non-constrained fields for your business and application use case.

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5 Document and maintain the schema

The final step is to document and maintain the schema in your database system. You should use comments, naming conventions, and metadata to describe the purpose, structure, and rules of your schema and queries. You should also use version control, backup, and recovery tools to track and protect the changes and updates of your schema and data. You should also monitor and optimize the performance, security, and quality of your schema and data over time.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you design SQL schemas that meet business requirements?

1

2

3

4

5

6

1 Analyze the business problem

2 Model the data

3 Choose the SQL dialect

4 Implement the schema

5 Document and maintain the schema

6 Here’s what else to consider

Database Development

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How do you design SQL schemas that meet business requirements?

1

2

3

4

5

6

1 Analyze the business problem

2 Model the data

3 Choose the SQL dialect

4 Implement the schema

5 Document and maintain the schema

6 Here’s what else to consider

Database Development

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