Event Quality Scanner

A small interactive tool that validates whether an event meets quality, naming, and domain rules.

This project is part of my Systems of Trust Series, exploring how distributed systems maintain truth, alignment, and consistency across event flows and microservices.

The goal of this scanner is to provide a simple, legible way to check whether a JSON event follows expected governance rules — before drift or inconsistencies show up downstream.

---

Purpose

Event quality issues create silent failures in distributed systems.
Even small discrepancies — wrong types, missing fields, naming mismatches — can cause:

• incorrect processing in downstream services
• broken dashboards
• inconsistent analytics
• confused debugging
• increased drift in event meaning over time

This scanner surfaces these issues clearly and early.

---

Features (MVP)

This prototype includes:

• Input box for a single JSON event
• Required-field verification
• Type validation for configured fields
• Naming checks (snake_case, camelCase, etc.)
• Domain rule validation (e.g., allowed values)
• Summary of pass/fail
• Human-readable list of issues

This tool is intentionally minimal and aimed at conceptual clarity.

---

Demo Screenshot

---

Quality Check Flow Diagram

         [Input JSON Event]
                  |
                  v
      Required Field Validator
      (presence + emptiness)
                  |
                  v
        Type & Format Validator
      ("123" vs 123, enum checks)
                  |
                  v
       Naming Convention Checker
      (snake_case vs camelCase)
                  |
                  v
        Domain Rule Validation
      (allowed values, patterns)
                  |
                  v
          Quality Summary
 (issues found, pass/fail status)

---

Why Event Quality Matters

Even small inconsistencies ripple through distributed systems:

• mismatched schemas between services
• harder reconciliation and joining
• incorrect metrics and dashboards
• misaligned contract assumptions
• unclear meaning during incidents
• ML models learning inconsistent patterns

This tool makes the earliest layer of trust legible: whether an event is even valid on its own.

---

How This Maps to Real Systems

Each component corresponds to a real architectural concern:

Required Fields

Missing fields often break pipelines or create ambiguous meaning.

Type Consistency

A number stored as a string can break aggregations, joins, or filters.

Naming Conventions

Teams evolving independently create schema forks (user_id, userId, userid).

Domain Rules

Certain fields must follow controlled vocabularies (e.g., “LOGIN”, “PURCHASE”).

Summary

A quick pass/fail gives teams clarity on whether the event meets its contract.

This scanner models a small but critical slice of event validation.

---

Part of the Systems of Trust Series

Main repo:
https://github.com/rtfenter/Systems-of-Trust-Series

---

Status

MVP implemented and active.
This scanner will focus on core mechanics required to demonstrate event quality checks, not full production validation.

---

Local Use

Everything runs client-side.

To run locally:

1. Clone the repo
2. Open index.html in your browser

That’s it — static HTML + JS, no backend required.