Your Data Quality is Broken. Here’s Why No One’s Talking About It.

Hello everyone, and welcome to another edition of ‘Patterns and Pipelines.’

I validated 1.4 million records in 4 seconds on my laptop yesterday—using a tool that didn’t exist three months ago. The validation failed—and that’s exactly what should have happened.

Here’s the uncomfortable truth: most data teams discover quality issues in production. A dashboard breaks. A report shows impossible numbers. An executive asks why revenue is negative. Then begins the scramble: combing logs, chasing upstream sources, reconstructing the past.

You’re not building data pipelines. You’re building archaeological sites for future debugging.

The Compliance Trap

Every regulated industry needs data validation. SOC2, HIPAA, GDPR—they all demand audit trails proving your data transformations are correct. So companies buy enterprise tools: Great Expectations, Monte Carlo, Datadog. These work, but they share three problems:

1. They’re expensive. $50k-$500k annually for tools that mostly run assertions.

2. They’re complex. Weeks of setup, Python DSLs, YAML everywhere.

3. They’re cloud-first. More vendors, more authentication, more points of failure.

Meanwhile, your startup’s data engineer just wants to know: “Did the customer file load correctly before I process it?” You don’t want your customers to find issues before you do.

What If Data Validation Was Boring?

That’s the entire philosophy behind PipeAudit. Validation should be:

• Fast enough to run inline

• Simple enough to configure in 5 minutes

• Trustworthy enough to show auditors

• Cheap enough that cost never enters the conversation

Here’s a complete validation contract:

[contract]
name = “people”
version = “0.1.0”
tags = [”pii”, “critical”]

[[columns]]
name = “id”
validation = [
  { rule = “not_null” },
  { rule = “unique” },
  { rule = “pattern”, pattern = “^CUST-[0-9]{6}$” }
]

[[columns]]
name = “age”
validation = [
  { rule = “range”, min = 0, max = 1000000 },
  { rule = “outlier_sigma”, sigma = 3.0 }
]

[source]
type = “s3”
location = “s3://data/people.csv”
profile = “prod”

That’s it. No Python classes. No inheritance hierarchies. No framework to learn.

Three Ways to Use It

CLI for data engineers:

pipa run people
# 📊 Read 153595302 bytes from S3
# 🔍 Starting validation with 153595302 bytes, extension csv
# ✅ Found driver for extension: csv
# ✅ Parsed DataFrame with 1425690 rows, 7 columns
# 📊 Contract people v0.1.0: 4 PASS, 3 FAIL

API for orchestrators:

curl -X POST http://localhost:8080/api/v1/run/people
# Returns 204 No Content, writes full audit trail

Library for custom pipelines:

import pipa

result = pipa.validate_contract(”people”)
if not result.passed:
    quarantine_data()
    alert_team()

All three produce identical audit logs. All three use the same validation engine. All three are compliance-ready.

The Compliance Answer

Every validation creates a cryptographically sealed audit log:

{
  “timestamp”: “2025-09-30T10:00:00Z”,
  “contract”: {”name”: “people”, “version”: “0.1.0”},
  “results”: [
    {”column”: “age”, “rule”: “Range”, “result”: “fail”, 
     “details”: “bad_count=143, min=0, max=60”}
  ]
}

These logs are:

• Tamper-proof (daily cryptographic sealing)

• Machine-readable (JSONL format)

• Auditor-friendly (maps directly to compliance requirements)

• Queryable (throw them in any lakehouse)

You don’t need a specialized compliance UI. The logs ARE the compliance artifact.

Why This Matters Now

AI and LLMs are creating a data quality crisis. Companies are:

• Ingesting unstructured data at unprecedented scale

• Building RAG pipelines on uncertain foundations

• Making decisions based on embeddings of potentially bad data

“Garbage in, garbage out” isn’t just a saying anymore—it’s a lawsuit waiting to happen.

PipeAudit doesn’t solve AI hallucinations. But it does ensure the data going INTO your AI systems is valid, complete, and audit-trailed. When your LLM gives a wrong answer, you can prove the input data was correct.

The Philosophy

Most data tools optimize for features. PipeAudit optimizes for trust.

Open source core? Yes—because you need to audit the auditor. Minimal API surface? Yes—because complexity is where bugs hide. Boring technology? Yes—because production systems need reliability, not excitement.

The cloud version adds convenience (hosting, UI, historical analytics), but the validation engine itself? That’s open. Auditable. Trustworthy.

Try It

Try It (Coming Soon)

When we launch in Q4 2025:

# Initialize project
pipa init my-validation

# Run example
cd my-validation
pipa run example

Or pull the Docker container:

docker run -v ./contracts:/contracts pipeaudit/free
curl -X POST http://localhost:8080/api/v1/run/example

Data quality isn’t sexy. Audit logs aren’t exciting. But when your CFO asks “How do we know this revenue number is correct?” you’ll have an answer that doesn’t start with “Well, we think...”

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Lokryn PipeAudit is in active development. The CLI and Docker free tier land first, with the cloud platform coming Q4 2025.