Veja como você pode criar um esquema de banco de dados para um novo projeto.

The method i recommend to define and create a new DB structure is: firstable, study and analyze starting from the most abstract point of view and then deep inside the business to know the full needs in the whole company.

The database of any product is very crucial part. It is a backbone of the entire product life cycle. Having said that, this backbone stands on understanding the main objectives and functionalities of the system/ product. So we should always start with understanding the needs of the application, gathering requirements with proper questionnaire with all the possibilities to the client.

When creating a new database schema, ensure it meets requirements by using appropriate data types for accuracy, estimating data volume for scalability, and optimizing queries and reports for performance. Support efficient data modification for smooth entry and updates. Optimize query responses for performance and plan for scalability to handle growth. Implement robust security measures, including encryption and access controls. Define and enforce relationships and constraints for data integrity. Apply normalization to reduce redundancy and improve integrity, and create indexes to enhance query performance.

To design an effective database schema, it's essential to have a clear understanding of the project's data requirements and how the data will be used. Schedule meetings with project stakeholders, including business analysts, developers, and end-users, to gather detailed information about the data entities, their attributes, and the relationships between them. Discuss the expected data volume, growth rate, and any performance or scalability requirements. For example, if you're designing a schema for an e-commerce platform, you'll need to consider entities like products, categories, orders, and customers. Engage with the sales and marketing teams to understand how they plan to use the data for reporting and analysis.

Before you dive into database design, you must thoroughly understand the project's requirements. Engage with stakeholders to gather all necessary information about the data to be stored and how it will be accessed. Consider the types of queries that will be run, the expected volume of data, and any specific constraints or relationships that need to be enforced. This initial stage is critical as it lays the foundation for your schema and influences all subsequent design decisions. Properly understanding these requirements ensures that the database will be efficient, scalable, and aligned with business needs, preventing costly redesigns or performance issues down the line.

Before diving into database design, thoroughly understand the project's requirements by engaging with stakeholders to gather detailed information about data storage and access needs. Identify query patterns, assess expected data volume and growth, and define necessary constraints and relationships. Estimate the types of queries, frequency of access, and performance needs. Document these insights clearly, including data entities, attributes, relationships, and business rules, along with the rationale behind design choices. This thorough understanding ensures your design aligns with business goals, supports efficient data access, and facilitates scalability and maintenance.

When i creating the db design, several points i need to be considered 1. User requirement 2. Syncronization with reports 3. Easy to maintain , mostly at the master table and the relational with transactional tables 4. Tables which will be significantly growing up interms of the space usage and need more attention on the maintenance to avoid any performance issues 5. Flexibilies to enhance if any new requirement needed

Once the requirements are clear, it is time to choose the structure like which database to use. We should be able to answer should we use relational database or NoSQL database. We should clarify this first and this decision should be basis on the requirements.

Once you have a clear understanding of the project requirements, decide on the database structure. Relational databases, like MySQL or PostgreSQL, are organized into tables and are ideal for data with clear relationships and structured formats. NoSQL databases, such as MongoDB, are suitable for unstructured data or when horizontal scaling is needed. Your choice will affect the schema design, so consider the data's nature, scalability requirements, and the complexity of interactions between data entities. Evaluate the specific use cases, query patterns, and performance needs to ensure the database structure aligns with the project's goals and future growth.

Depends on client budget and for the existing db, check on the version and the space available. Several db recommended 1. Oracle 2. Mssql 3. Mysql

After understanding the project requirements, the next step is to identify the main entities that will form the building blocks of your database schema. Each entity represents a distinct object or concept within the system and will be mapped to a table in a relational database or a collection in a NoSQL database. Take the time to carefully define the attributes for each entity, specifying their data types, constraints, and any default values. In a project management application, you might have entities such as "Project" ,"Task" ,"User" and "Team". The "Project" entity may have attributes like "project_id" "name" "description" "start_date" and "end_date".

In relational databases, relationships are represented using primary and foreign keys. A primary key uniquely identifies each record within a table, while a foreign key refers to the primary key of another table, creating a link between the two entities. By carefully defining these relationships, you ensure data integrity, avoid redundancy, and enable efficient querying of related data.

After designing the schema, it's crucial to test it thoroughly using sample data and common queries. This helps identify performance issues and any need for indexes or adjustments in relationships and normalization. For example, if queries are slow, adding indexes on frequently accessed columns can improve retrieval speed. Testing also ensures data integrity and consistency across various scenarios like insertions, updates, and deletions. Based on the results, refine the schema iteratively to optimize performance and meet the project's growing needs.