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Build a Data Quality Validator

Data Quality Validator — a complete walkthrough of data quality enforcement, covering rule engine, column profiler, uniqueness checker, and testing.

data_quality_validator/
├── src/index.ts
├── src/rule-engine.ts
├── src/column-profiler.ts
├── tests/data_quality_validator.test.ts
└── package.json

// Rule engine: data quality enforcement core
export function ruleengine(input: RuleengineInput): RuleengineResult {
  // Build the rule engine that anchors data quality enforcement and defines the core data shape.
  return processRuleengine(input);
}

// Column profiler — handles data quality enforcement Add the column profiler so
export class Columnprofiler {
  constructor(private opts: ColumnprofilerOptions) {}
  async run(ctx: Context): Promise<ColumnprofilerOutput> {
    return this.execute(ctx);
  }
}

// Uniqueness checker: data quality enforcement stage
export const uniquenesschecker = (next: Next) => async (req: Request) => {
  const out = await transform(req);
  return next(out);
};

// Range checker for data quality enforcement
export async function rangechecker(stream: AsyncIterable<Chunk>): Promise<Aggregate> {
  let acc = init();
  for await (const c of stream) acc = merge(acc, c);
  return acc;
}

// Report emitter: data quality enforcement finalization
export function reportemitter(state: State): Result {
  validate(state);
  return finalize(state);
}

Project overview

This project builds a data quality enforcement system whose reason for existing is checking a dataset against rules for nulls, ranges, and uniqueness before it is consumed downstream. The temptation in data quality enforcement is to bolt the interesting part onto a working prototype and discover the hard cases in production; this build inverts that order and treats a uniqueness rule over a column with whitespace variants that treats trimmed and untrimmed values as distinct as the first thing designed, not the last.

You will implement Rule engine, Column profiler, Uniqueness checker, Range checker, and Report emitter, each as a module with one responsibility and a test that proves it in isolation. The data quality enforcement pipeline is then the wiring that composes them, and the integration test is where the composition is shown to survive a uniqueness rule over a column with whitespace variants that treats trimmed and untrimmed values as distinct.

The walkthrough is written for a developer who has used a data quality enforcement system but has not had to make one honest. It favors explicit data shapes over clever abstractions, and it names the failure each design choice prevents rather than presenting the choice as a style preference.

Each phase ends with something observable — a rule engine that returns the right shape, a column profiler that rejects bad input, an integration test that goes green — so you can stop at any point and still have a data quality enforcement system that works for the cases covered so far.

What you will build

The finished data quality enforcement system exposes five capabilities, each with a focused test:

• Rule engine
• Column profiler
• Uniqueness checker
• Range checker
• Report emitter

Rule engine is the foundation that every later stage assumes; Column profiler is where data quality enforcement correctness lives; Uniqueness checker handles a uniqueness rule over a column with whitespace variants that treats trimmed and untrimmed values as distinct; Range checker makes the system observable; and Report emitter is the graceful-degradation layer that keeps overload from becoming corruption.

The build keeps the data quality enforcement logic in ordinary modules you can read in one sitting, and pulls in dependencies only for the parts that are genuinely tedious to hand-roll. That keeps the diff between the tutorial and a real codebase small, and keeps the failure modes visible in code rather than hidden behind a framework.

Architecture and data model

The system is organized around Rule engine, Column profiler, and Uniqueness checker, with Range checker tying them together. Data flows as a set of rules evaluated per column, producing a pass-fail report with counts of violating rows and sample offenders.

Each component is a module with a pure interface where practical: the same input produces the same output, and side effects are pushed to the edges. That discipline is what makes the phases independently testable, because a test for Uniqueness checker does not need to stand up Rule engine and Column profiler to exercise the interesting branch.

The data model is deliberately explicit. A set of rules evaluated per column, producing a pass-fail report with counts of violating rows and sample offenders Keeping the shape written down — in a schema, a type, or a fixture — means a change to the shape is a visible diff rather than a silent runtime shift, and it means the integration test can assert against a concrete shape instead of guessing.

Configuration and code are kept apart: the configuration says what data quality enforcement should do, the modules say how. A reviewer can change a threshold or a policy without reading the implementation, which keeps the risky changes small and lets the diff stand as the review.

The failure path through Uniqueness checker is designed before the happy path is finished, because a uniqueness rule over a column with whitespace variants that treats trimmed and untrimmed values as distinct is the case that distinguishes a data quality enforcement system from a demo. Every component declares what it does when its inputs are wrong, and the integration test asserts each of those declarations.

Prerequisites and dependencies

Have these ready before you install anything, so the data quality enforcement setup is not blocked by a missing tool:

• Node.js 20 LTS or later
• Familiarity with data quality enforcement fundamentals
• A local environment to run the test suite

The runtime dependencies are chosen to keep the data quality enforcement datapath legible:

• fastify (^5.0.0) — Validation API
• csv-parse (^5.6.0) — Dataset parsing

# setup for data-quality-validator
pnpm init
pnpm add fastify csv-parse
pnpm add -D typescript tsx vitest

Versions are pinned to a major range so the data quality enforcement install is reproducible; commit the lockfile. The rule engine reads its operational parameters from the environment and validates them at startup, so a missing value fails the boot rather than the first data quality enforcement request.

Directory structure

Keep each data quality enforcement component in its own file so it can be tested without starting the others:

data_quality_validator/
├── src/index.ts
├── src/rule-engine.ts
├── src/column-profiler.ts
├── tests/data_quality_validator.test.ts
└── package.json

Phase 1: Rule engine

Build the rule engine that anchors data quality enforcement and defines the core data shape.

// Rule engine: data quality enforcement core
export function ruleengine(input: RuleengineInput): RuleengineResult {
  // Build the rule engine that anchors data quality enforcement and defines the core data shape.
  return processRuleengine(input);
}

After this phase the rule engine works in isolation and is covered by a unit test. The rule engine defines the data shape that every later stage assumes, so getting it right first means the later phases compile against a stable contract.

Treat the rule engine as the single source of truth for the data quality enforcement data shape. Validate inputs at the boundary and reject early, so a malformed value never reaches Column profiler or Uniqueness checker where its effect would be harder to localize.

Phase 2: Column profiler

Add the column profiler so data quality enforcement can checking a dataset against correctly.

// Column profiler — handles data quality enforcement Add the column profiler so
export class Columnprofiler {
  constructor(private opts: ColumnprofilerOptions) {}
  async run(ctx: Context): Promise<ColumnprofilerOutput> {
    return this.execute(ctx);
  }
}

After this phase the column profiler works in isolation and is covered by a unit test. This is where data quality enforcement correctness lives: the column profiler is what makes checking a dataset against rules for nulls, ranges, and uniqueness before it is consumed downstream true, and its test is the proof.

Keep the column profiler pure where you can. A function that takes an input and returns an output is trivial to test and impossible to mock wrong; a class with hidden state is the opposite. Push side effects to the caller and keep the column profiler about the transformation.

Phase 3: Uniqueness checker

Wire the uniqueness checker to handle a uniqueness rule over without losing data.

// Uniqueness checker: data quality enforcement stage
export const uniquenesschecker = (next: Next) => async (req: Request) => {
  const out = await transform(req);
  return next(out);
};

After this phase the uniqueness checker works in isolation and is covered by a unit test. The uniqueness checker is the difference between a data quality enforcement system that works on the happy path and one that survives a uniqueness rule over a column with whitespace variants that treats trimmed and untrimmed values as distinct.

Design the failure before the success. Decide what uniqueness checker does when a uniqueness rule over a column with whitespace variants that treats trimmed and untrimmed values as distinct: reject, retry, shed, or degrade — and write that test first. A data quality enforcement system whose failure behavior is unspecified will pick one at random under pressure, and it is usually the wrong one.

Phase 4: Range checker

Integrate the range checker to make data quality enforcement observable and testable end to end.

// Range checker for data quality enforcement
export async function rangechecker(stream: AsyncIterable<Chunk>): Promise<Aggregate> {
  let acc = init();
  for await (const c of stream) acc = merge(acc, c);
  return acc;
}

After this phase the range checker works in isolation and is covered by a unit test. Observability is not a finishing touch; it is how you know the data quality enforcement system is doing what it claims in production.

Emit one structured event per meaningful action, with enough context to localize a failure without a second round-trip. The range checker is what turns “something is slow” into “the column profiler is slow for inputs matching this shape,” which is the difference between an alert and an investigation.

Phase 5: Report emitter

Finalize the report emitter so the data quality enforcement system degrades gracefully under overload.

// Report emitter: data quality enforcement finalization
export function reportemitter(state: State): Result {
  validate(state);
  return finalize(state);
}

After this phase the report emitter works in isolation and is covered by a unit test. The report emitter is what keeps overload from becoming corruption: when the data quality enforcement system is asked for more than it can do, it degrades in a defined way instead of failing in an undefined one.

Pick a degradation strategy for report emitter — shed, queue, or backpressure — and make it explicit in code and in a test. Leaving the behavior to chance is how a data quality enforcement traffic spike becomes a data loss incident.

Testing

Each phase has a focused test: rule engine, column profiler, and uniqueness checker are exercised in isolation, then an integration test drives the full data quality enforcement pipeline and asserts the a uniqueness rule over case is handled.

Start with unit tests that pin each component’s contract: the rule engine returns the documented shape, the column profiler transforms correctly, and the uniqueness checker fails in the documented way. These tests are fast and they localize regressions to the component that broke.

Then add an integration test that composes the components and drives a realistic data quality enforcement scenario end to end. Assert the happy path first, then deliberately trigger a uniqueness rule over a column with whitespace variants that treats trimmed and untrimmed values as distinct and assert the system degrades rather than corrupts. Because the components are pure where practical, the integration test stays deterministic rather than flaky.

A data quality enforcement suite must also prove the negative: bad input is rejected at the boundary, overload is shed rather than queued indefinitely, and a partial failure does not leave the system in an inconsistent state. These are the tests that catch the regressions a happy-path suite never would.

The entire data quality enforcement suite runs with no external service; every dependency is either in-process or stubbed, so every case is reproducible in CI. That is what lets the uniqueness checker failure-mode assertions live as tests rather than as a manual checklist that drifts out of date.

# run the data quality enforcement test suite
pnpm vitest run

Deployment and operations

Run at least two data quality enforcement instances behind a load balancer and share state in Redis. Set resource limits so data quality enforcement overload surfaces as backpressure rather than failure.

Run at least two stateless instances behind a load balancer, and move any shared state into a shared store so the data quality enforcement behavior is global rather than per instance. Size limits so the system sheds load before it runs out of memory, because a data quality enforcement system that OOMs under load fails in a way that is hard to reason about.

Drain on deploy: stop accepting new work, let in-flight data quality enforcement operations finish, then exit. Because the system holds no state that cannot be reconstructed, a rollout is a matter of cycling instances one at a time while watching the error rate.

When a dependency is unavailable, decide explicitly whether the data quality enforcement system fails closed or degrades. Leaving that choice to chance is how a dependency outage becomes a silent data loss, so make the choice visible in code and in a test.

Security considerations

Validate all data quality enforcement inputs at the boundary, reject oversized payloads before buffering them, and keep secrets in the environment rather than the repository.

The data quality enforcement system is a choke point, so it is the right place to reject malformed input before it reaches column profiler or uniqueness checker. Validate the shape and the size at the edge, and let the inner components assume what they receive is well-formed.

Never log secrets or full payloads; log enough to localize a failure without recording data that should not be on disk. Treat any external call as untrusted and validate its response before using it, because a data quality enforcement system that trusts its inputs blindly is one malformed response away from corruption.

Keep dependencies pinned and run an audit in CI. A transitive dependency with a known vulnerability is still a vulnerability in your data quality enforcement system, and the lockfile is what makes the audit meaningful.

Observability

Emit one structured event per data quality enforcement operation with the inputs’ shape, the outcome, and a correlation id. Track error rate and latency per component and alert on a step change rather than a single spike.

The correlation id is the thread that ties a user-visible symptom to the data quality enforcement decision and to the component log entry for the same operation. Without it, an investigation starts from a timestamp and a guess.

Watch the uniqueness checker failure rate separately from the overall error rate. A jump in uniqueness checker failures usually means a uniqueness rule over a column with whitespace variants that treats trimmed and untrimmed values as distinct, not a bug in column profiler, and the two have different owners and different fixes.

Chart the share of data quality enforcement operations that shed, queue, or succeed. A change in the mix points at the cause faster than an aggregate latency can, because each class has a different root.

Troubleshooting

A uniqueness rule over a column with whitespace variants that treats trimmed and untrimmed values as distinct. Reproduce with a minimal fixture, then add a guard test so the data quality enforcement system rejects the condition loudly.

Performance regresses under data quality enforcement load. Profile with the sampling analyzer, locate the hot path in column profiler, and add a targeted benchmark to lock the regression.

Keep the data quality enforcement dashboards separate from dependency dashboards. When an operation fails, the correlation id lets you decide in seconds whether the fault is rule engine, column profiler, or uniqueness checker, instead of guessing from a timeout.

Limitations and next steps

• Single-region; multi-region replication is out of scope.
• The rule engine is in-process and not horizontally shared by default.
• No auth layer; assume deployment behind an edge that enforces identity.

The data quality enforcement system is single-region by design; extending to multi-region means making rule engine shareable across regions, which is a real change to the consistency model rather than a config flag. The uniqueness checker is in-process, which is correct for one instance but must move to a shared store before scaling out.

There is no authentication or authorization layer; the system assumes it is deployed behind an edge that enforces identity. Adding one is straightforward but out of scope for this build, and doing it here would distract from the data quality enforcement correctness that is the point.

The natural next step, once the data quality enforcement pipeline is trusted in production, is to add a short-TTL cache for idempotent reads and a richer range checker that exports histograms rather than counters. Both are additive and neither changes the contracts this build establishes.