How can you develop and administer databases to meet industry standards?

as solid experiences DBA , they required as followed -monitor and tune the database performance and availability on a regular basis - update and upgrade the database software and hardware as needed -have a clear understanding of the business requirements and objectives of the database system -implement and enforce database standards and policies for naming, coding, security, backup, and performance -test and validate the database functionality and performance before deploying it to production

1. Understand Industry-Specific Standards: Depending on your industry, there may be specific standards and regulations that dictate how data should be handled. Examples include GDPR for personal data in the EU, HIPAA for health information in the US, and PCI DSS for payment card data. Ensure you're familiar with the standards relevant to your industry and geography. 2.Security Measures: Encryption: Use encryption both at rest and in transit Access Control: Implement role-based access controls, ensuring only authorized personnel can access or modify the database. Regular Audits: Periodically review and audit permissions and roles.

Very basic idea in the era of big data and complex business scenarios, adopting Domain-Driven Design (DDD) for data modeling is a game-changer. This approach meticulously aligns the database structure with intricate business domains. By ensuring the model mirrors real-world scenarios, it offers a robust foundation. Moreover, as businesses evolve, DDD ensures your data model remains adaptable, reducing the need for frequent overhauls and ensuring long-term sustainability. This alignment between business and data is the hallmark of modern, agile enterprises.

This is a vvvvvvvvvvv important phase. Many dun understand. After system is live, we get tons of performance problems as data volume gets bigger. And what do we do ? "Create more indexes on table, get more memory, get more cores. Not enough, get new server!" Basic normalisation isn't done!

Create a robust data model using entity-relationship diagrams (ERDs) to define data entities, relationships, and constraints. Ensure the model supports business requirements and adheres to normalization principles to reduce redundancy.

we sometimes have to postpone indexing strategies after developers write queries due to knowing about which columns are suitable for index.

The future is multi-model databases. These innovative systems support multiple data models, such as document, graph, and relational, all within a single, cohesive backend. This versatility means they can efficiently handle a diverse range of data types and intricate relationships. By adopting multi-model databases, organizations can ensure they're equipped to handle both current and emerging data challenges, offering unparalleled flexibility and scalability, a necessity in today's rapidly changing digital landscape.

Design for scalability by considering data partitioning, sharding, and the use of distributed databases. Incorporate security measures in the design phase, such as encryption for sensitive data, access control mechanisms, and data masking.

The fusion of Database Development and DevOps is revolutionizing the industry. By integrating database changes into a DevOps pipeline, there's a seamless blend of continuous integration and delivery. This integration not only enhances the speed and quality of database development but also drastically reduces manual intervention and error rates. As businesses demand faster, more reliable database deployments, Database DevOps is no longer a luxury; it's a necessity.

It would be troublesome if you have an old server that has many users with high privileges. It might affect your strategies. In this case, you might not be able to limit them at once

Use version control systems for database schema changes to track and manage modifications. Implement consistent coding standards for SQL and database scripts to enhance readability and maintainability. Use stored procedures and functions for complex business logic to ensure consistency and security. Optimize queries and database structures (e.g., indexing) during development to ensure efficient data retrieval and updates.

The next frontier in database performance is Automated Database Tuning. Modern Database Management Systems (DBMSs) are now equipped with machine learning capabilities. These systems can auto-tune performance by dynamically adapting configurations based on real-time workloads. This reduces the time-consuming and often error-prone process of manual tuning. By leveraging this technology, businesses can ensure their databases run optimally, adapting in real-time to workload changes, and ensuring consistent, peak performance.

Use performance monitoring tools to track database performance metrics such as query response times, CPU usage, memory usage, and disk I/O. Regularly analyze and maintain indexes to ensure optimal query performance.

The first major task is thinking of backup methods and prepare disaster recovery checklist. It is helpful to have an instruction to follow in condition of data loss. You must be nervous under that circumstance so be aware of it before it happens.

In an age of increasing cyber threats, database security is paramount. A holistic approach, combining encryption, authentication, authorization, and regular audits, is essential. But beyond these measures, it's crucial to stay updated with global data protection regulations like GDPR, HIPAA, or PCI-DSS. Non-compliance can lead to hefty fines and reputational damage. Moreover, embracing a Zero Trust architecture, which assumes no trust by default and verifies every request, can further bolster database security, ensuring data integrity and trustworthiness.

Only authorized persons are allowed to access the server. Rights should be based on the object level. Logging and tracking should be implemented.

Implement role-based access control (RBAC) to limit access to sensitive data based on user roles and responsibilities. Use encryption for data at rest and in transit to protect against unauthorized access and breaches. Mask or anonymize sensitive data in non-production environments to protect privacy during development and testing.

Plan for failover clustering, backups, replication and any other procedures necessary to ensure that the database structure remains intact.

Maintain detailed documentation for database schemas, configurations, procedures, and policies. This includes ER diagrams, data dictionaries, and operational procedures. Provide regular training for database administrators, developers, and users on best practices, security policies, and compliance requirements.

A DR test is not just restoring a database and ticking a box. When (not if) DR is invoked the DBA is almost certainly going to presented with an unknown position. The skill here is being able to correctly troubleshoot any scenario and have the wherewithal to plan the recovery. Too many companies perform a restore and convince themselves they are adequately prepared in the event of DR. They are not.

Working with git, git action and tool for database versioning such as flyway will help lot for automate and align status of any datbases from dev, test and prod environnement

To emphasize what Sebastian said I have an example: While testing our Azure MS SQL Server backups (using an external tool) I discovered that there is a bug in Azure SQL Server which will not let you restore a "bacpac" export if your database has an Azure AD user defined. The workaround was to copy the DB, remove the users and then backup. Many of these external tools do not support scripting the backup. There are also some issues with DB encryption that you need to be aware of. Test your backups!

Other things that you should consider in reality is database version and how do you keep scripts in different versions of your database. Another matter is about separation between test database (test stage before production) and production database.