You're facing constant database modifications. How do you ensure data reliability remains intact?

Regularly perform consistency checks to identify and rectify data corruption or anomalies. Use change management processes to document, review, and approve database modifications. Use automated testing frameworks to continuously test database functionalities and performance. Use encryption for data at rest and in transit to protect against unauthorized access and data breaches.

Defining standards is crucial for maintaining database reliability. Establishing a set of rigorous data management standards is the first line of defense against reliability issues. These standards should cover naming conventions, data types, and formats to ensure consistency across the database. Implementing a robust Data Definition Language (DDL) can help enforce these standards. DDL is a syntax used to define data structures, such as tables, in a database. By using DDL, you can automate the creation and modification of database objects, reducing the risk of human error and ensuring that every change adheres to your predetermined standards.

Trying to define standards on industry solutions can help whether is Finance, Assets , Health and many more can be fine tuned and reviewed when looking at what type of Assets and Finance. Health can be more complex but there are ISO standards you can start looking .The global standards that can be used will help minimise impact on future changes and design. Geography such as longitude and latitude is always a good option to use with address data but is not available or difficult to get accurate information but is very useful to implement as a standard. Data dictionaries can help as reference table as well using meaningful industry names that conform to multiple Database SQL ANSI standards will help with portable code and data migrations.

Schemas, stored procedures , functions can be versioned and stored in repositories. Having the ability to implement a DevOps environment will help and noted changes should be well documented including authors, dates and what the changes include or even removed In terms of functionality.

Unit testing can be easily Test led using scripts or test software. A regression test can be more difficult to implement but should be done by the expert user and then capturing the testing in automation software that can be re run to check for Performance, data issues or bugs. An expected result set of data is the key to reviewing and signing off. So for example a complex payroll process or conversion will need to have the same finanal results with the ability to review the results, sometimes reviewing the results also can be automated as the checking can be time Consuming.

It depends how Critical the backs need to be . For example one can have a live replicated data set to a backup or Geolocation different to where the current database is located. Most cloud databases allow for this. Backups need to be verified and possibly encrypted for Safety reasons to be protected for internal hackers. Offsite back still occur by tape usually or a dedicated vendors who has an authorised person and procedure to pickup the backups daily or weekly. Backups and shipping logs are useful and one can restore up to the point of time before any corruption. Audit changes or change data capture CDC can then be used to Recover and review the corrupt data

Most cloud systems have excellent monitoring systems including alerts, attempts of denial of service and security. Just need to turn these on and pay for the service on the critical systems in particular. Performance monitoring can actually slow The system so need to work out for example EOM to create a test system and simulate issue and fix before trying in production.