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Mohammad Waseem
Mohammad Waseem

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Mastering Dirty Data Cleaning with TypeScript and Open Source Tools

#typescript #datasecurity #opensource

Mastering Dirty Data Cleaning with TypeScript and Open Source Tools

Data quality is a perennial challenge in software systems, especially when ingesting data from external sources. Dirty data—containing inconsistencies, nulls, malformed entries, or security vulnerabilities—can compromise system integrity, analytics, and user trust. Addressing this problem effectively requires not only rigorous validation but also scalable and maintainable cleaning strategies.

As a security researcher and seasoned developer, I have explored leveraging TypeScript alongside open source tools to automate and enhance data cleaning processes. TypeScript’s static typing, combined with powerful libraries, provides a robust environment for implementing scalable, error-resistant data sanitization workflows.

The Approach: Combining TypeScript with Open Source Data Libraries

The core idea revolves around defining structured data schemas, validating incoming data, and normalizing or sanitizing it to prevent security issues like injection attacks or malformed data entry. For this purpose, I utilize libraries such as zod for schema validation, lodash for data manipulation, and AJV for JSON schema validation. These tools complement TypeScript's type system, creating a resilient pipeline for cleaning dirty data.

Step 1: Define Data Schemas with zod 

import { z } from 'zod';

const UserSchema = z.object({
  name: z.string().min(1),
  email: z.string().email(),
  age: z.number().int().positive().optional(),
  comments: z.string().optional()
});

type User = z.infer<typeof UserSchema>;
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This schema enforces data types, required fields, and basic validation rules.

Step 2: Validate and Sanitize Data 

const rawData = {
  name: 'John Doe',
  email: 'john..doe@example.com',
  age: '27', // incorrect type
  comments: '<script>alert(1)</script>'
};

// Validate with zod
try {
  const validatedUser = UserSchema.parse(rawData);
  // Sanitize comments to prevent XSS
  validatedUser.comments = sanitizeHtml(validatedUser.comments || '');
  console.log('Clean data:', validatedUser);
} catch (err) {
  console.error('Validation error:', err.errors);
}

function sanitizeHtml(input: string): string {
  // Basic sanitization using DOMPurify or similar library
  // For demonstration, replace script tags
  return input.replace(/<script[^>]*>.*?<\/script>/gi, '');
}
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This step ensures invalid data is caught early and malicious scripts are stripped out.

Step 3: Extend with JSON Schema Validation for Complex Structures 

import Ajv from 'ajv';

const ajv = new Ajv();
const schema = {
  type: 'object',
  properties: {
    name: { type: 'string' },
    email: { type: 'string', format: 'email' },
    age: { type: 'integer', minimum: 1 },
  },
  required: ['name', 'email'],
};
const validate = ajv.compile(schema);

const dataToValidate = { name: 'Alice', email: 'alice@domain.com', age: 30 };

if (validate(dataToValidate)) {
  console.log('Data is valid and cleaned');
} else {
  console.error('Validation errors:', validate.errors);
}
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Final Remarks

Integrating these tools creates a reliable pipeline for cleaning dirty data, reducing security risks, and enhancing data integrity. By leveraging TypeScript's type safety and open source validation libraries, developers and security researchers can craft scalable solutions tailored to complex data ecosystems.

This approach is adaptable across domains—from healthcare to finance—making it a versatile strategy for improving data hygiene. Regular updates and community support for these tools further ensure that your data cleaning processes evolve with emerging threats and data standards.

References

Ensuring clean, safe data ingestion not only safeguards your infrastructure but also builds trust with your users. Embracing these open source solutions within a TypeScript environment can dramatically improve your data management workflows.

🛠️ QA Tip

To test this safely without using real user data, I use TempoMail USA.

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