dqguard 0.3.0

Lightweight data quality validation framework for big data pipelines

DataGuard

Lightweight data quality validation for big data pipelines

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DataGuard is a lightweight data quality validation framework that works with both Pandas and PySpark. Define rules declaratively, set pass-rate thresholds, and get rich reports — without the boilerplate.

Install

pip install dqguard          # Pandas engine
pip install dqguard[spark]   # With PySpark support

Quick Start

import pandas as pd
from dataguard import DataGuard, RuleSet, not_null, in_range, in_set, regex_match

df = pd.DataFrame({
    "name": ["Alice", "Bob", "Charlie", None, "Eve"],
    "age": [25, 30, -1, 40, 150],
    "email": ["[e-mail]", "invalid", "[e-mail]", "[e-mail]", "[e-mail]"],
    "status": ["active", "active", "inactive", "active", "unknown"],
})

rules = RuleSet()
rules.add("name", not_null())
rules.add("age", in_range(0, 120))
rules.add("email", regex_match(r"^[\w.-]+@[\w.-]+\.\w+$"))
rules.add("status", in_set(["active", "inactive"]))

report = DataGuard(df).validate(rules)
print(report.summary())

DataGuard Validation Report
Engine: pandas
Total Rules: 4 | Passed: 0 | Failed: 4
Overall Status: INVALID
------------------------------------------------------------
[FAIL] name.not_null | pass_rate=80.00% (threshold=100%) | 4/5 rows passed
[FAIL] age.in_range(0, 120) | pass_rate=80.00% (threshold=100%) | 4/5 rows passed
[FAIL] email.regex_match(...) | pass_rate=80.00% (threshold=100%) | 4/5 rows passed
[FAIL] status.in_set(...) | pass_rate=80.00% (threshold=100%) | 4/5 rows passed

Features

Threshold-based validation

Not every column needs 100% compliance. Set thresholds per rule:

rules.add("middle_name", not_null(), threshold=0.95)    # allow 5% nulls
rules.add("transaction_id", unique(), threshold=0.999)   # 99.9% unique

Data profiling

profile = DataGuard(df).profile()
for col, stats in profile.items():
    print(f"{col}: {stats['distinct_count']} distinct, {stats['null_rate']:.2%} nulls")

RuleSet from dict

Define rules as data — useful for config-driven pipelines:

from dataguard import RuleSet

config = {
    "name": [{"check": "not_null"}],
    "age": [{"check": "in_range", "params": {"min_val": 0, "max_val": 120}}],
    "email": [{"check": "regex_match", "params": {"pattern": r"^[\w.-]+@[\w.-]+\.\w+$"}}],
}
rules = RuleSet.from_dict(config)

CLI

# Validate a CSV file against a rule config
dqguard validate data.csv --rules rules.json

# Profile a dataset
dqguard profile data.csv

JSON export

print(report.to_json())   # for CI/CD integration

PySpark

from pyspark.sql import SparkSession
from dataguard import DataGuard, RuleSet, not_null, in_range

spark = SparkSession.builder.appName("dqguard").getOrCreate()
df = spark.read.parquet("s3://my-bucket/data/")

rules = RuleSet()
rules.add("user_id", not_null())
rules.add("age", in_range(0, 120))

report = DataGuard(df).validate(rules)

Built-in Checks

Check	Description
not_null()	Value must not be None/NaN
unique()	Column values must be unique
in_range(min, max)	Numeric value within range (inclusive)
regex_match(pattern)	String matches regex pattern
in_set(values)	Value in allowed set
min_length(n)	String has at least n characters
max_length(n)	String has at most n characters
custom(fn, name)	Custom validation function

Project Structure

dataguard/
├── __init__.py          # Public API
├── core.py              # DataGuard main class
├── rules.py             # Rule & RuleSet definitions
├── checks.py            # Built-in check functions
├── report.py            # ValidationReport & ValidationResult
├── exceptions.py        # Custom exceptions
├── pandas_engine.py     # Pandas validation backend
├── spark_engine.py      # PySpark validation backend
└── cli.py               # CLI entry point

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

License

MIT License — see LICENSE for details.