Baselinr Project Overview

📁 Complete Project Structure

profile_mesh/
│
├── baselinr/                 # Main Python package
│   ├── __init__.py
│   ├── cli.py                  # Command-line interface
│   │
│   ├── config/                 # Configuration management
│   │   ├── __init__.py
│   │   ├── schema.py          # Pydantic models
│   │   └── loader.py          # YAML/JSON config loader
│   │
│   ├── connectors/            # Database connectors
│   │   ├── __init__.py
│   │   ├── base.py           # Abstract base connector
│   │   ├── postgres.py       # PostgreSQL implementation
│   │   ├── snowflake.py      # Snowflake implementation
│   │   └── sqlite.py         # SQLite implementation
│   │
│   ├── profiling/            # Profiling engine
│   │   ├── __init__.py
│   │   ├── core.py          # Main profiling orchestrator
│   │   └── metrics.py       # Column-level metric calculator
│   │
│   ├── storage/             # Results storage
│   │   ├── __init__.py
│   │   ├── writer.py       # Results writer
│   │   └── schema.sql      # Storage schema DDL
│   │
│   ├── drift/              # Drift detection
│   │   ├── __init__.py
│   │   └── detector.py     # Drift detector and reporter
│   │
│   └── integrations/
│       └── dagster/         # Dagster orchestration
│           ├── __init__.py
│           ├── assets.py    # Asset factory
│           ├── sensors.py   # Plan-aware sensor
│           └── events.py    # Event emission
│
├── examples/                # Example configurations
│   ├── config.yml          # PostgreSQL config
│   ├── config_sqlite.yml   # SQLite config
│   ├── dagster_repository.py  # Dagster definitions
│   └── quickstart.py       # Quickstart script
│
├── docker/                 # Docker development environment
│   ├── docker-compose.yml  # Compose configuration
│   ├── Dockerfile         # Application container
│   ├── init_postgres.sql  # Database initialization
│   ├── dagster.yaml      # Dagster instance config
│   └── workspace.yaml    # Dagster workspace config
│
├── tests/                 # Test suite
│   ├── __init__.py
│   ├── test_config.py    # Configuration tests
│   └── test_profiling.py # Profiling tests
│
├── setup.py              # Package setup (setuptools)
├── pyproject.toml        # Modern Python packaging config
├── requirements.txt      # Python dependencies
├── Makefile             # Development automation
├── .gitignore          # Git ignore patterns
├── .dockerignore       # Docker ignore patterns
├── LICENSE             # Apache License 2.0
├── README.md           # Main documentation
├── docs/getting-started/QUICKSTART.md       # Quick start guide
├── DEVELOPMENT.md      # Developer guide
├── PROJECT_OVERVIEW.md # This file
└── MANIFEST.in        # Package manifest

🎯 Key Features Implemented

✅ Phase 1 MVP Complete

All Phase 1 requirements from the specification have been implemented:

1. Profiling Engine ✓

• ✅ Profiles tables via SQLAlchemy
• ✅ Collects schema metadata
• ✅ Computes column metrics:
  • count, null %, distinct %
  • min, max, mean, stddev
  • histograms
  • string length statistics
• ✅ Supports sampling
• ✅ Outputs structured results (JSON + SQL)

2. Configuration System ✓

• ✅ YAML/JSON configuration loader
• ✅ Pydantic validation
• ✅ Warehouse connection configuration
• ✅ Table patterns (explicit or wildcard-ready)
• ✅ Sampling configuration
• ✅ Output destination configuration
• ✅ Environment overrides via env vars

3. Storage Layer ✓

• ✅ Results table with history
• ✅ Schema includes:
  • dataset_name, column_name
  • metric_name, metric_value
  • profiled_at, run_id
• ✅ Runs table for metadata
• ✅ Automatic table creation

4. Execution Layer ✓

• ✅ CLI command: baselinr profile --config config.yml
• ✅ Dagster integration:
  • Dynamic asset factory
  • Configurable jobs
  • Event emission
  • Schedule definitions

5. Developer Environment ✓

• ✅ Docker Compose setup with:
  • PostgreSQL (sample data + results)
  • Dagster daemon
  • Dagster web UI
• ✅ Sample data generator (SQL seed script)
• ✅ No-cost local setup
• ✅ Sample tables: customers, products, orders

6. Drift Detection ✓

• ✅ Compare two profile runs
• ✅ Detect schema changes
• ✅ Calculate metric differences
• ✅ Severity classification (low/medium/high)
• ✅ JSON output
• ✅ Summary statistics

📊 Supported Databases

Database	Status	Notes
PostgreSQL	✅ Full	Primary development target
SQLite	✅ Full	Lightweight local testing
Snowflake	✅ Full	Enterprise data warehouse
MySQL	🔲 Easy	Can be added with connector
BigQuery	🔲 Easy	Can be added with connector
Redshift	🔲 Easy	Can be added with connector

🔧 Available Commands

CLI Commands

# Profile tables
baselinr profile --config config.yml [--output results.json] [--dry-run]

# Detect drift
baselinr drift --config config.yml --dataset <name> \
  [--baseline <run-id>] [--current <run-id>] \
  [--output report.json] [--fail-on-drift]

Makefile Commands

make help           # Show all commands
make install        # Install Baselinr
make docker-up      # Start Docker environment
make docker-down    # Stop Docker environment
make quickstart     # Run quickstart example
make test           # Run tests
make format         # Format code
make lint           # Run linters

Python API

from baselinr.config.loader import ConfigLoader
from baselinr.profiling.core import ProfileEngine
from baselinr.storage.writer import ResultWriter
from baselinr.drift.detector import DriftDetector

# Load config
config = ConfigLoader.load_from_file("config.yml")

# Profile tables
engine = ProfileEngine(config)
results = engine.profile()

# Write results
writer = ResultWriter(config.storage)
writer.write_results(results)

# Detect drift
detector = DriftDetector(config.storage)
report = detector.detect_drift(dataset_name="customers")

🚀 Getting Started

Choose your path:

1. Quick Test (5 minutes)

cd profile_mesh
make docker-up
pip install -e ".[dagster]"
make quickstart

2. Full Setup (10 minutes)

cd profile_mesh
make install-all
make docker-up
# Wait 30 seconds
baselinr profile --config examples/config.yml

3. Your Database

• Copy examples/config.yml
• Update connection details
• Add your tables
• Run: baselinr profile --config your_config.yml

📚 Documentation Files

File	Purpose
README.md	Main documentation and feature overview
docs/getting-started/QUICKSTART.md	Step-by-step getting started guide
DEVELOPMENT.md	Architecture and contribution guide
PROJECT_OVERVIEW.md	This file - project structure

🧪 Testing

# Run all tests
make test

# Run specific test file
pytest tests/test_config.py -v

# Run with coverage
pytest --cov=baselinr tests/

🐳 Docker Environment

The Docker environment includes:

• PostgreSQL (port 5432)

  • Database: baselinr
  • User: baselinr
  • Password: baselinr
  • Sample tables pre-loaded

• Dagster UI (port 3000)

  • http://localhost:3000
  • Pre-configured with Baselinr assets
  • Daily schedule for profiling

📦 Package Distribution

Baselinr can be installed as:

# Basic installation
pip install baselinr

# With Snowflake support
pip install baselinr[snowflake]

# With Dagster orchestration
pip install baselinr[dagster]

# Full installation
pip install baselinr[all]

# Development mode
pip install -e ".[dev,all]"

🎯 Phase 1 Completion Criteria - STATUS

All criteria from the specification are met:

✅ CLI works: baselinr profile --config config.yml produces results
✅ Dagster integration: Assets discoverable and runnable
✅ Storage: Results written to structured tables
✅ Drift detection: Can compare two profile runs

🔮 Future Enhancements (Post-MVP)

Phase 2

• Web dashboard for visualization
• Alert system (email, Slack, PagerDuty)
• Additional database connectors
• Enhanced drift detection (ML-based)
• Data quality rules engine

Phase 3

• Column correlation analysis
• PII detection
• Data lineage tracking
• Integration with data catalogs
• Real-time profiling

📄 License

Apache License 2.0 - see LICENSE file for details.

🤝 Contributing

Contributions welcome! See DEVELOPMENT.md for guidelines.

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Baselinr v0.1.0 - MVP Complete ✅