A horror story that hasn't happened to you yet — and why I spent 847 hours making sure it never will
Let me tell you a story.
It's 2:47 AM on a Tuesday.
You're asleep. Your phone buzzes. Then buzzes again. Then it won't stop.
It's your CFO:
"The quarterly report is wrong. The board meets in 6 hours. Fix it."
Your stomach drops.
You validated that data. Twice. You wrote the SQL. You checked the row counts. You even spot-checked random samples.
Everything looked perfect.
But somewhere between your data warehouse and the final report, 47,000 transactions silently duplicated themselves.
No errors. No warnings. No alerts.
Just... wrong numbers. Very wrong numbers.
$2.3 million wrong.
Now, let me be honest.
This hasn't happened to me.
But I've seen it happen. I've watched teams scramble at midnight. I've read the postmortems. I've seen careers damaged by "data issues."
And every single time, the story is the same:
"We didn't know. The data looked fine."
That's what terrifies me.
Not the bugs you can see — the ones that crash your system, throw exceptions, page you immediately.
The bugs that scare me are the silent ones.
The ones that whisper.
The Lies Data Tells
Think about your production data right now.
Are you sure it's correct?
- That "unique" ID column — is it actually unique?
- That "required" field — does it have hidden NULLs?
- That email column — how many contain "test@test.test"?
- That price field — any negative values? Any with 17 decimal places?
- That date column — any timestamps from 1970? From 2099?
Each lie is tiny. Easy to miss. Invisible in spot checks.
Until they compound.
Until the quarterly report is wrong.
Until someone asks, "Why didn't we catch this?"
The Moment I Decided to Build This
I was reviewing a data pipeline last year.
Nothing special — just a routine ETL job. Data comes in, gets transformed, goes out.
I asked: "How do we know this data is correct?"
The answer:
# Actual production code I found
assert len(df) > 0, "Data exists"
assert "user_id" in df.columns, "Required column exists"
# LGTM! Ship it! 🚀
That was it.
That was the "validation."
I started digging into other pipelines. Same story. Everywhere.
- No uniqueness checks
- No type validation beyond "it loaded"
- No statistical bounds
- No pattern matching
- No referential integrity
Just vibes. Just "it worked yesterday, so it's probably fine."
I imagined that 2:47 AM phone call.
And I thought: Not me. Not on my watch.
So I Started Building
I wanted something that didn't exist.
A framework that could look at data and see the problems — without me having to specify every single rule.
Something like:
# What I wanted
import magic_validator
results = magic_validator.check("data.csv")
# "Hey, I found 47 problems you didn't know about"
The existing tools couldn't do this.
Great Expectations? Powerful, but you have to define every expectation manually. For 200 columns, that's pages of YAML.
Pandas validation? Too primitive. df["age"] > 0 doesn't catch an age of 847.
Custom scripts? Every team has dozens. None of them talk to each other.
So I built what I wanted.
847 hours later, it became Truthound.
Truthound v1.0.0
├── 275+ validators
├── 22 categories
├── 25+ data sources
├── 4,300+ tests
└── Zero tolerance for lying data
I named it Truthound.
Because it hounds your data for the truth.
(Yes, I'm proud of that pun. No, I won't apologize.)
The Philosophy: Data Should Prove Its Innocence
Most validation tools ask:
"Tell me what to check, and I'll check it."
Truthound asks differently:
"Show me your data. I'll tell you what's wrong with it."
import truthound as th
# One line. Zero configuration.
results = th.check("sales_data.parquet")
# Truthound automatically detects:
# - Column types and validates accordingly
# - Patterns (emails, phones, URLs, IPs...)
# - Statistical anomalies
# - Referential integrity issues
# - And 270+ other potential problems
It's not magic. It's pattern recognition + statistical analysis + years of collected wisdom about how data lies.
What It Actually Does
It Learns Your Data
# Feed it your known-good data
rules = th.learn("clean_historical_data.parquet")
# It generates validation rules automatically
# "This column is always positive integers between 1-1000"
# "This field matches email pattern with 99.7% consistency"
# "These two columns have a 1:N relationship"
It Catches What Humans Miss
# Run against new data
results = th.check("daily_import.csv", rules=rules)
# Results:
# ⚠️ Column 'price': 3 values below historical minimum
# ❌ Column 'email': 47 values don't match learned pattern
# ⚠️ Column 'user_id': Referential integrity violation (847 orphans)
# ❌ Column 'timestamp': 12 values are in the future
It Scales Without Sweating
| Dataset Size | Check Time | Memory |
|---|---|---|
| 10M rows | < 10 sec | < 2GB |
| 100M rows | < 100 sec | < 4GB |
Built on Polars. Lazy evaluation. Rust performance.
Your laptop can validate datasets that would make Pandas cry.
Remember That Horror Story?
The 2:47 AM phone call. The $2.3 million error. The duplicated transactions.
Here's how Truthound would have prevented it:
import truthound as th
# Profile the source data
source_profile = th.profile("warehouse_transactions.parquet")
# Profile the transformed data
result_profile = th.profile("report_transactions.parquet")
# Compare
diff = th.compare(source_profile, result_profile)
# Output:
# ❌ CRITICAL: Row count mismatch
# Source: 1,847,233 rows
# Result: 1,894,233 rows
# Difference: +47,000 rows (2.5% inflation)
#
# ❌ CRITICAL: Duplicate detection
# Column 'transaction_id' has 47,000 duplicates
# Expected: unique
#
# 💰 Estimated impact: Revenue overcounted by $2,340,847
Three lines of code.
The horror story stays a story.
The Architecture (For the Curious)
I built this in 10 phases. Each one solving a real problem:
Phase 1-3: Core Validation Engine
- 275+ validators across 22 categories
- Custom validator SDK — build your own in minutes
- ReDoS protection — because malicious regexes are real
- i18n support — error messages in 7 languages
Phase 4: Storage Layer
- Results to S3, GCS, Azure, or local filesystem
- Automatic versioning and retention policies
- Hot/warm/cold tiering for historical data
- Cross-region replication for enterprise
Phase 5: Every Data Source You Use
- Files: CSV, Parquet, JSON, Excel
- Databases: PostgreSQL, MySQL, SQLite, Oracle, SQL Server
- Cloud Warehouses: BigQuery, Snowflake, Redshift, Databricks
Phase 6: CI/CD Integration
- GitHub Actions, GitLab CI, Jenkins, and 9 more platforms
- Slack, Teams, PagerDuty, Discord, Telegram notifications
- Fail your builds when data quality fails
Phase 7: Auto-Profiling
- Statistical profiling in milliseconds
- Automatic rule generation from good data
- Schema evolution detection — know when columns change
Phase 8: Beautiful Documentation
- HTML reports with 5 themes (dark mode included)
- 15 languages with proper pluralization
- PDF export that doesn't look like 2003
- White-labeling for enterprise deployments
Phase 9: Plugin System
- Build custom validators, reporters, data sources
- Security sandbox for untrusted plugins
- Hot reload without restart
- Full documentation generation
Phase 10: Advanced Detection
- ML-based anomaly detection
- Data drift monitoring over time
- Real-time streaming validation (Kafka, Kinesis)
- Data lineage tracking with visualization
What's Coming Next
The core framework is complete. The ecosystem is just beginning.
Building Now (Phase 11-17):
| Feature | Description |
|---|---|
| 🔌 Workflow Integration | Airflow, dbt, Dagster, Prefect operators |
| 🌐 Web Dashboard | REST API + visual interface |
| 🔐 Enterprise Auth | OAuth 2.0 / SSO / SAML |
| 📚 Governance | Business glossary, data catalog |
| ✅ Compliance | SOC 2 toolkit |
Separate packages, clean dependencies:
truthound-airflowtruthound-dashboardtruthound-governance
The core stays lightweight. The ecosystem grows.
Try It Now
pip install truthound
# Check any file instantly
th check your_data.csv
# Generate a beautiful report
th check data.parquet --format html -o report.html
# Learn rules from trusted data
th learn historical_data.parquet -o rules.json
# Enforce those rules going forward
th check new_data.csv --rules rules.json
GitHub: github.com/seadonggyun4/Truthound
Why I Built This
Every company claims to make "data-driven decisions."
But how many verify that the data itself is trustworthy?
Bad data doesn't announce itself. It doesn't throw exceptions. It doesn't wake you up at night.
It just sits there. Quietly. Poisoning every report, every model, every decision built on top of it.
Until one Tuesday at 2:47 AM, when your phone won't stop buzzing.
I built Truthound so that phone call never comes.
I built it so the horror story stays a story.
I built it so you can sleep at night — knowing your data is telling the truth.
Your data is talking. Truthound helps you hear what it's really saying.
Top comments (0)