Dynamic Data Validation

Dynamic data validation is a process that involves validating data against a dynamic set of rules or conditions. This process enables more flexibility and adaptability in data validation, as the validation rules can be adjusted on-the-fly based on the specific requirements.

This often involves passing a list of validation functions to another function, which applies all of these validation checks to the data. Each validation function returns a boolean value indicating whether the data passed the validation check.

Here's some sample data in a JavaScript array:

let data = [
  { name: "John", email: "john@email.com", age: "30" },
  { name: "Jane", email: "jane@email", age: "25" },
  { name: "Bob", email: "bob@email.com", age: "40" },
  { name: "Alice", email: "alice@email.com", age: "forty" },
];

We now need a function that can accept an arbitrary number of validation functions. This allows for the caller to configure validation rules dynamically instead of needing to hardcode the logic in advance.

function validateEmail(email) {
  let re = /\S+@\S+\.\S+/;
  return re.test(email);
}
function validateAge(age) {
  return !isNaN(age);
}
function dynamicValidation(data, validators) {
  return data.map((row) => {
    for (const validator of validators) {
      if (!validator(row)) {
        row.isValid = false;
        return row;
      }
    }
    row.isValid = true;
    return row;
  });
}
const validators = [
  (row) => validateEmail(row.email),
  (row) => validateAge(row.age),
];
data = dynamicValidation(data, validators);

In this example, dynamicValidation is a function that accepts a data array and an array of validation functions. It applies each validator to every row in the data array, marking the row as invalid if any of the validators return false.

Certainly, here are the before and after tables represented in markdown:

Before

After

In the after table, we have an additional isValid column that indicates whether a given row has passed all the specified validation checks. In this case, Jane's email doesn't meet the validation because it lacks a top-level domain, and Alice's age is invalid because it's not a numerical value.

• The order of the validators may matter. If one validator relies on the result of another, they need to be ordered appropriately.
• You might need to implement error handling to ensure that if a validator fails (for example, due to an error in the validator itself), the validation process can continue, or at least fail gracefully.
• Consider the performance implications. The more validators you add, the longer the validation process may take.

• Flagging Invalid Rows: After dynamic data validation, you might flag invalid rows for further review or cleaning.
• Dropping Invalid Rows: If a validation check fails, you might want to drop a row altogether.