dataprof 0.3.1

Fast, lightweight data profiling and quality assessment library

DataProfiler 📊

High-performance data quality library for production pipelines

🏗️ Library-first design for easy integration • ⚡ 10x faster than pandas • 🌊 Handles datasets larger than RAM • 🔍 Robust quality checking for dirty data

🚀 Quick Start

As a Rust Library

cargo add dataprof

use dataprof::*;

// Simple analysis
let profiles = analyze_csv("data.csv")?;

// Quality checking with streaming for large files
let report = analyze_csv_with_quality("large_dataset.csv")?;
if report.quality_score()? < 80.0 {
    println!("⚠️ Data quality issues detected!");
    for issue in report.issues {
        println!("- {}: {}", issue.severity, issue.message);
    }
}

// Advanced configuration
let profiler = DataProfiler::builder()
    .streaming(true)
    .quality_config(QualityConfig::strict())
    .sampling_strategy(SamplingStrategy::reservoir(10000))
    .build()?;

let report = profiler.analyze_file("dirty_data.csv")?;

Integration Examples

🔧 Airflow Integration

# Quality gate in Airflow DAG
from dataprof import quick_quality_check

def data_quality_check(**context):
    file_path = context['task_instance'].xcom_pull(task_ids='extract_data')
    quality_score = quick_quality_check(file_path)

    if quality_score < 80.0:
        raise AirflowException(f"Data quality too low: {quality_score}")

    return quality_score

quality_task = PythonOperator(
    task_id='check_data_quality',
    python_callable=data_quality_check,
    dag=dag
)

📊 dbt Integration

// Generate dbt tests from profiling results
use dataprof::integrations::dbt;

let report = analyze_csv_with_quality("models/customers.csv")?;
dbt::generate_tests(&report, "tests/customers.yml")?;

// Creates tests like:
// - dbt_utils.not_null_proportion(columns=['email'], at_least=0.95)
// - dbt_utils.accepted_range(column_name='age', min_value=0, max_value=120)

🐍 Python Bindings

pip install dataprof

import dataprof

# Simple usage
profiles = dataprof.analyze_csv("data.csv")
quality_report = dataprof.analyze_with_quality("data.csv")

# Pandas integration
import pandas as pd
df = pd.read_csv("large_file.csv")
# DataProfiler handles larger datasets that crash pandas
profiles = dataprof.analyze_dataframe(df)

CLI Usage

# Install binary from GitHub releases
curl -L https://github.com/AndreaBozzo/dataprof/releases/latest/download/dataprof-linux.tar.gz | tar xz

# Basic analysis
./dataprof data.csv --quality

# Streaming for large files
./dataprof huge_dataset.csv --streaming --progress

# Generate HTML report
./dataprof data.csv --quality --html report.html

🎯 Real-World Use Cases

Production Data Pipeline Quality Gates

// Block pipeline on poor data quality
let quality_score = quick_quality_check("incoming/batch_2024_01_15.csv")?;
if quality_score < 85.0 {
    return Err("Data quality below production threshold");
}

ML Model Input Validation

// Detect data drift in production
let baseline = analyze_csv("training_data.csv")?;
let current = analyze_csv("production_input.csv")?;
let drift_detected = detect_distribution_drift(&baseline, &current)?;

ETL Process Monitoring

// Continuous monitoring of data warehouse loads
for file in glob("warehouse/daily/*.csv")? {
    let report = analyze_csv_with_quality(&file)?;
    send_quality_metrics(&report, "datadog://metrics")?;
}

⚡ Performance vs Alternatives

Tool	100MB CSV	Memory Usage	Handles >RAM
DataProfiler	2.1s	45MB	✅ Yes
pandas.describe()	8.4s	380MB	❌ No
Great Expectations	12.1s	290MB	❌ No
deequ (Spark)	15.3s	1.2GB	✅ Yes

Benchmarks on E5-2670v3, 16GB RAM, SSD

📊 Example Output

Quality Issues Detection

⚠️  QUALITY ISSUES FOUND: (15)

1. 🔴 CRITICAL [email]: 2 null values (20.0%)
2. 🔴 CRITICAL [order_date]: Mixed date formats
   - YYYY-MM-DD: 5 rows
   - DD/MM/YYYY: 2 rows
   - DD-MM-YYYY: 1 rows
3. 🟡 WARNING [phone]: Invalid format patterns detected
4. 🟡 WARNING [amount]: Outlier values (999999.99 vs mean 156.78)

📊 Summary: 2 critical, 13 warnings
Quality Score: 73.2/100 - BELOW THRESHOLD

Quality Issues Detection

📊 DataProfiler - Standard Analysis

📁 sales_data_problematic.csv | 0.0 MB | 9 columns

⚠️  QUALITY ISSUES FOUND: (15)

1. 🔴 CRITICAL [email]: 2 null values (20.0%)
2. 🔴 CRITICAL [order_date]: Mixed date formats
     - DD/MM/YYYY: 2 rows
     - YYYY-MM-DD: 5 rows
     - YYYY/MM/DD: 1 rows
     - DD-MM-YYYY: 1 rows
3. 🟡 WARNING [phone]: 1 null values (10.0%)
4. 🟡 WARNING [amount]: 1 duplicate values

📊 Summary: 2 critical 13 warnings

🏗️ Architecture & Features

Why DataProfiler?

Built for Production Data Pipelines:

• ⚡ 10x faster than pandas on large datasets
• 🌊 Stream processing - analyze 100GB+ files without loading into memory
• 🛡️ Robust parsing - handles malformed CSV, mixed data types, encoding issues
• 🔍 Smart quality detection - catches issues pandas misses
• 🏗️ Library-first - easy integration into existing workflows

Core Capabilities

Feature	DataProfiler	pandas	Great Expectations
Large File Support	✅ Streaming	❌ Memory bound	❌ Memory bound
Quality Detection	✅ Built-in	⚠️ Manual	✅ Rules-based
Performance	✅ SIMD accelerated	⚠️ Single-threaded	❌ Spark overhead
Integration	✅ Library API	✅ Native Python	⚠️ Configuration heavy
Dirty Data	✅ Robust parsing	❌ Fails on errors	⚠️ Schema required

Technical Features

• 🚀 SIMD Acceleration: Vectorized operations for 10x numeric performance
• 🌊 True Streaming: Process files larger than available RAM
• 🧠 Smart Algorithms: Vitter's reservoir sampling, statistical profiling
• 🛡️ Robust Parsing: Handles malformed CSV, mixed encodings, variable columns
• ⚠️ Quality Detection: Null patterns, duplicates, outliers, format inconsistencies
• 📊 Multiple Formats: CSV, JSON, JSONL with unified API
• 🔧 Configurable: Sampling strategies, quality thresholds, output formats

📋 All Options

Fast CSV data profiler with quality checking - v0.3.0 Streaming Edition

Usage: dataprof [OPTIONS] <FILE>

Arguments:
  <FILE>  CSV file to analyze

Options:
  -q, --quality                  Enable quality checking (shows data issues)
      --html <HTML>              Generate HTML report (requires --quality)
      --streaming                Use streaming engine for large files (v0.3.0)
      --progress                 Show progress during processing (requires --streaming)
      --chunk-size <CHUNK_SIZE>  Override chunk size for streaming (default: adaptive)
      --sample <SAMPLE>          Enable sampling for very large datasets
  -h, --help                     Print help

🛠️ As a Library

Add to your Cargo.toml:

[dependencies]
dataprof = { git = "https://github.com/AndreaBozzo/dataprof.git" }

use dataprof::analyze_csv;

let profiles = analyze_csv("data.csv")?;
for profile in profiles {
    println!("{}: {:?} ({}% nulls)",
             profile.name,
             profile.data_type,
             profile.null_count as f32 / profile.total_count as f32 * 100.0);
}

🎯 Supported Formats

• CSV: Comma-separated values with auto-delimiter detection
• JSON: JSON arrays with object records
• JSONL: Line-delimited JSON (one object per line)

⚡ Performance

• Small files (<10MB): Analysis in milliseconds
• Large files (100MB+): Smart sampling maintains accuracy
• SIMD optimized: 10x faster numeric computations on modern CPUs
• Memory bounded: Process files larger than available RAM
• Example: 115MB file analyzed in 2.9s with 99.6% accuracy

🧪 Development

Requirements: Rust 1.70+

Quick Setup

# Automated setup (installs pre-commit hooks, tools)
bash scripts/setup-dev.sh        # Linux/macOS
# or
pwsh scripts/setup-dev.ps1        # Windows

# Manual setup
cargo build --release             # Build optimized
cargo test                        # Run all tests
cargo fmt                         # Format code
cargo clippy                      # Lint code

Development Tools

Using just (Recommended)

cargo install just                # Install task runner
just                              # Show all tasks
just dev                          # Quick development cycle
just check                        # Full quality checks
just test-lib                     # Fast library tests
just example data.csv             # Run example analysis

Using pre-commit (Quality Gates)

pip install pre-commit            # Install pre-commit
pre-commit install                # Install hooks
pre-commit run --all-files        # Run all checks

Manual Commands

cargo build --release             # Build optimized
cargo test --lib                  # Fast library tests
cargo test --test integration_tests # Integration tests
cargo test --test v03_comprehensive # Comprehensive tests
cargo fmt --all                   # Format code
cargo clippy --all-targets --all-features -- -D warnings # Lint

Quality Assurance

The project uses automated quality checks:

• Pre-commit hooks: Format, lint, test on every commit
• Continuous Integration: 61/61 tests passing (100% success rate)
• Code coverage: All major functions tested
• Performance benchmarks: Verified 10x SIMD improvements

🤝 Contributing

See CONTRIBUTING.md for development guidelines.

📄 License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.