How can you design a database that is both maintainable and upgradable?

Normalization: Minimize redundancy through organized data tables. Documentation: Clearly document schema, relationships, and rules. Version Control: Track changes and roll back if needed. Modular Design: Divide the database into logical modules. Consistent Naming: Follow naming conventions for clarity. Views and Stored Procedures: Simplify complex queries and operations. Data Migration Scripts: Ensure smooth transitions during changes. Backup and Recovery: Establish routines for data safety. Testing Environment: Simulate upgrades before applying to production. Security Measures: Prioritize robust security protocols.

Before diving into database object creation, establish a clear vision by defining project scope, user roles, and specifications. This involves thoughtful consideration of data models, logical structures, and physical designs to anticipate and prevent complications. Strategic planning upfront minimizes the need for later adjustments and ensures a smoother development process.

utiliza un modelo relacional bien estructurado, normalizado para reducir redundancias, y documenta adecuadamente la estructura y las relaciones. Implementa procedimientos de respaldo regulares y planifica cuidadosamente cualquier cambio para minimizar impactos en la integridad y el rendimiento de los datos.

In my experience, it starts with the main goal of the project. I've faced with some databases in my workplaces that has became far from their real role over time and save much unnecessary data just because they want to be comprehensive but you just focus on what you need then try to expand it with your requirement. First of all, define your main entities in your databases and relations between them (of course in relational model) , try to consider tables for them and fill them on paper then imagine how to fetch them depend on your needs! This first draft helps you to do it from scratch.

First instance, You must have an Interview with the customer, get all the documentation which You can understand the core and rules of business is the key. Through E-R model entity, design the model based in all documentation which brings the real world inside of our model, You never must forget about normalization concepts to design simple, effective and lasting databases with minimum maintenance

In my experience, Maintaining consistency in database design is pivotal; adhere to standards like using prefixes (e.g., tbl_ for tables) and consistent naming styles (camel case or snake case) for clarity.

Maintaining consistency in database design is pivotal; adhere to standards like using prefixes (e.g., tbl_ for tables) and consistent naming styles (camel case or snake case) for clarity. Employing conventions and commenting code ensures easier comprehension, maintenance, and seamless upgrades in your database. Documenting schema and logic adds another layer of understanding for efficient collaboration and troubleshooting.

It is a great concept that I have encountered in my career—to prefix table names with the main entity they represent. Here is an example of creating a schema for a college: Each table representing student will begin with the 'ST_' prefix, such as: ST_STUDENT ST_APPLICATIONS ... And for college employees, it will be: E_EMPLOYEE E_PAST_SALARIES ... It tremendously help with writing queries later on.

Never be disorganized! These rules might present you very strict but I have to confess that they are vital even though somebodies call them just discipline.

Following standardized practices is equally vital. Consistency in naming conventions, formatting, and coding enhances the database's clarity and ease of maintenance. Utilize prefixes or suffixes to denote object types, such as tbl_ for tables, and adopt a consistent style, whether camel case or snake case, for naming conventions. Additionally, document the code comprehensively, providing insights into the database schema and logic for better understanding.

Normalization, ranging from 1NF to 5NF, organizes data to reduce redundancy and dependencies, enhancing integrity and query optimization. Employ techniques like foreign keys for table linkage, steer clear of storing derived values, and eliminate repeating groups for effective normalization without sacrificing performance or usability. This process ensures a streamlined database structure, minimizing anomalies and facilitating efficient data management.

🔍 Key Insights on Data Normalization Across Databases - Normalization Universality: Vital for both SQL and NoSQL databases, including document-based systems like Elasticsearch and MongoDB. - Elasticsearch: Utilizes sharding for efficient large data set management. - MongoDB: Enhances performance through indexing frequently used fields. - Normalization Balance: Avoid over-normalization to prevent complexity and performance issues. Strive for the right balance for data integrity and efficiency. - Schema-Application Alignment: Ensure that application capabilities align with any changes in database schemas. - Robust Backup Plans: Critical for both databases and applications, enabling safe rollbacks if needed.

Ensure proper data normalization, organizing information into tables and adhering to normalization rules, from 1NF to 5NF. Use foreign keys to establish relationships between tables, eliminate redundancy, and avoid storing calculated values. This comprehensive approach, encompassing planning, standardization, and normalization, establishes a solid foundation for a database that is not only maintainable but also easily upgradable as the system evolves.

Normalization to 4th NF form should be mandatory if you are using any kind of relational databases. So as to optimize the queries and remove the redundancies.

There are no silver bullets, but in my personal opinion, the amount of energy and query time wasted due to improper database normalization is way too high. Third Normal Form (3NF) is a gold standard that you should always strive to achieve. Denormalization to Second Normal Form (2NF) is a very rare scenario, which you should challenge until no better option is found.

Optimizing database performance hinges on selecting precise data types for columns; choose integer, decimal, or float for numeric, char, varchar, or text for textual, and date, time, or timestamp for temporal values. Consider size, precision, and scale to align data types with the nature and range of your data, steering clear of generic or ambiguous types like blob or varbinary. This ensures efficient storage, accurate representation, and improved overall database functionality.

Try to use smaller one at first and fit it with what you need not use bigger one just because of future demands. Be greedy to choose the length. Another important matter that you should consider is the characteristics of datatypes in each DBMS for example I confronted with some issues in timestamp in mysql just because developers don't know about the nature of it. This type doesn't contain zone in mysql so when I restored my database in another server all timestamp values change based on server timezone and it occurred problems in application UI. Sql server has comprehensive datetime types and you can use datatimeoffset So what you should consider is searching about them before definition.

Para modelar um banco de dados capaz de suportar sua aplicação e suas mudanças, você precisa conhecer quais dados ela irá produzir. Além disso, é importante ter uma estrutura que seja capaz de suportar análise de dados.

Data types can be crucial while insertion, updating or even while reading from the database because if you use wrong data types you need to convert every bit of the data from the application and it will make your application slow sometimes.

It is a rare occasion to design a new database from scratch. You should always treat this opportunity with the utmost care, as the database is the real foundation for every app. Database engine selection - Different RDBMS can share common concepts, but they are quite distinct in details such as transaction control, ACID or clustering, to name a few. What kind of traffic will the database serve? - Generally, a database can be described as OLTP (Online Transaction Processing) or Data Warehouse/OLAP, and often they are a mix of those but physical design will favor only one of them. For for Data Warehouses, you will design models called Star and Snowflake with Fact and Dimension tables which are not present in OLTP. Planning is the key.

While implementing indexes is important for queries, one must consider the impact of those indexes to inserts/updates/deletes. Proper testing is necessary to validate that the correct number of indexes are being used for the way your database uses tables. It is a delicate dance to provide useable indexes and ensure transactions on the table aren’t hindered.

DBA can prevent these inconsistencies but I usually see the lack of error handling and showing the proper text error messages so try to communicate with technical lead in applications to prepare a comprehensive error handler class for application unless you accept that you explain to developers many times that why they can't save their rows in database!

Leveraging indexes on frequently queried columns enhances database speed by providing efficient data access. Constraints, such as uniqueness or foreign key rules, maintain data integrity and prevent invalid entries. Strategically apply these tools, creating indexes for high-usage columns and using constraints to ensure data quality and referential integrity based on your specific data characteristics and query patterns.

Proper indexing should be considered while considering the databases. So as to maximize the seeks and minimize the scans on a particular table.

What many DBA and Architects do not do is you can drop indexes when needed and add them back. I realize this can be hard with VLDB installations/applications but for those databases that are not in that class, think about this.

One of the most important aspects here is to avoid building performance issues into the code to begin with. Don't use RBAR methods and know what constitutes "Hidden RBAR". Learn what makes WHERE and JOIN criteria "SARGable". Use "Divide'n'Conquer" methods and remember that "Set-Based" does NOT necessarily mean "All in one query". And for those getting ready to quote Knuth's parable about "Pre-optimization is the root of all evil", I agree but that does NOT mean that you need to be lackadaisical with the likes of DATATYPEs or doing things right the first time. As a bit of a sidebar, make sure that your code is properly commented so that others can reduce their troubleshooting time and that includes you 6 months from now.

To design a maintainable and upgradable database: Maintain normalization, use consistent naming, document thoroughly, implement version control, establish backup and recovery plans, maintain a testing environment, plan for data migration, adhere to standards, ensure scalability, and prioritize security measures.

I'm the opinion of having three stages of database develop, test and product . these environments helps us to control issues. It also helps to find the problems beforehand. You can focus on performance in product environment becuase it has real data but if you have a good bench mark and enough resources in your test environment, it helps you to examine it

In section 5 above, Avahita Heidarnezhad alluded to one of the most important aspects of any database related project and most people skip the first and most important step... Too often, a DBA that knows what they're doing is added to the project team only after the can has been kicked way too far down the road. Step 1 should always be, "Put a DBA on the project even before any other requirements of the project are identified".

Designing maintainable and upgradable database involves careful planning and adherence to best practices. For example normalising the database, documenting the database thoroughly, established regular backup and recovery procedures, consider scalability from the beginning, and testing the database helps catch potential issues early.

Resist to adding unnormalized tables or denormalizing tables. It's like doing bad renovations practices on a house. You are going to fall into a spiral of ever growing costs in all aspects that refers to handling this data. Implement "contract tables". A "contract table" hides forever the database implantation to the application. These are tables fed by dedicated functions, to turn data in a contract form. The "contract" is often a temporary denormalized version of the data. It also has extra non-data columns and validation result columns as a way to make the application communicate with the database (aside from regular data). As you can expect from this architecture, what executes the contract? A trigger.