lakelogic 0.1.0b2

A Python-based data contract runtime for consistent quality across engines.

LakeLogic

Trust Your Data. Scale Your Logic.

Write Once. Run Anywhere. — The open-source runtime for data contracts with quarantine.

LakeLogic is a SQL-first, infrastructure-agnostic quality gate that ensures your business decisions are based on data you can trust. It scales your validation logic from local Polars to petabyte-scale Spark without rewriting a single rule.

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The Core Value: Write Once. Run Anywhere

Stop paying the "Infrastructure Lock-In Tax." In a traditional stack, moving from a Warehouse (Snowflake) to a Lakehouse (Databricks) means months of rewriting validation rules. LakeLogic decouples your Business Logic from your Execution Engine.

1. Cost Efficiency (The Spark Tax ROI): Run 80% of your maintenance checks on Polars or DuckDB for pennies, while reserving Spark for your massive production scales.
2. Risk Mitigation (100% Reconciliation): Ensure Source = Good + Quarantined. Mathematically prove that no record was lost or double-counted across your layers.
3. Stakeholder Trust (Visual Traceability): Use aggregate roll-ups to give your business users a visual drill-down from board-level KPIs back to raw source records.

Key Features

• SQL-First Logic: Use the SQL expressions you already know for transformations and quality rules.
• Schema Enforcement: Type casting, required fields, and unknown-field handling.
• Intelligent Quarantine: Records that fail rules are detoured, tagged with error messages, and saved for correction.
• Lineage Injection: Tag records with source path, run ID, and processing timestamp.
• Materialization: Write validated data to local CSV/Parquet targets or Delta/Iceberg when running on Spark.
• Referential Integrity: Validate keys against dimensions using local reference tables.
• Notifications (Demo): Built-in adapters log alerts for quarantine and rule failures.
• External Logic Hooks: Run dedicated Python modules or notebooks for advanced Gold processing.
• 🆕 Delta Lake Support (Spark-Free): Read/write/merge Delta tables with Polars, DuckDB, or Pandas—no Spark required!
• 🆕 Catalog Table Names: Use Unity Catalog, Fabric LakeDB, and Synapse table names (catalog.schema.table) directly.

Installation

# Get the full engine suite
uv pip install "lakelogic[all]"

# Or just use Polars for local speed
uv pip install "lakelogic[polars]"

# Delta Lake support (Spark-free)
uv pip install "lakelogic[delta]"

# Profiling + PII detection (bootstrap)
uv pip install "lakelogic[profiling]"

See the full installation guide in docs/installation.md.

Quick Start

# 1. Run the Quality Gate (Automatic Engine Selection)
processor = DataProcessor(contract="silver_crm_customers.yaml")
source_df, good_df, bad_df = processor.run_source("bronze_crm_customers.csv")

# good_df -> Ready for Silver Layer
# bad_df  -> Sent to Quarantine

🆕 Delta Lake & Catalog Support (Spark-Free!)

Unity Catalog (Databricks)

from lakelogic import DataProcessor

# Use Unity Catalog table names directly (no Spark required!)
processor = DataProcessor(engine="polars", contract="contracts/customers.yaml")
source_df, good_df, bad_df = processor.run_source("main.default.customers")

# LakeLogic automatically:
# 1. Resolves table name to storage path
# 2. Uses Delta-RS for fast, Spark-free operations
# 3. Validates data with your contract rules

print(f"Total: {len(source_df)} | Valid: {len(good_df)} | Invalid: {len(bad_df)}")

Fabric LakeDB (Microsoft)

# Use Fabric table names directly
processor = DataProcessor(engine="polars", contract="contracts/sales.yaml")
source_df, good_df, bad_df = processor.run_source("myworkspace.sales_lakehouse.customers")

print(f"Total: {len(source_df)} | Valid: {len(good_df)} | Invalid: {len(bad_df)}")

Synapse Analytics (Azure)

# Use Synapse table names directly
processor = DataProcessor(engine="polars", contract="contracts/sales.yaml")
source_df, good_df, bad_df = processor.run_source("salesdb.dbo.customers")

print(f"Total: {len(source_df)} | Valid: {len(good_df)} | Invalid: {len(bad_df)}")

Learn more: Delta Lake Support | Catalog Table Names

Get Started

📚 Read the Docs | 🚀 Quickstart Guide | 💬 Discussions

Run Your First Contract (5 Minutes)

# Clone the repo
git clone https://github.com/LineageLogic/LakeLogic.git
cd LakeLogic/examples/01_getting_started/basic_validation

# Run the example
lakelogic run --contract contract.yaml --source data/sample_customers.csv

You'll see:

• ✅ Good records that passed validation
• ❌ Quarantined records with error reasons
• 📊 Quality metrics and health scores

Explore 90+ Examples

The examples/ directory contains runnable examples organized by skill level:

• Getting Started - Your first contract in 5 minutes
• Tutorials - Medallion architecture, reference joins, notifications
• Patterns - Bronze quality gates, SCD2, deduplication, late-arriving data
• Production - Complete insurance ELT pipeline with multi-entity contracts
• Integrations - Airflow, Prefect, Dagster, Databricks job templates

Documentation

• Full Documentation - Complete guides and API reference
• How It Works - Medallion architecture and core concepts
• CLI Reference - Command-line usage
• API Reference - Python API documentation
• Reprocessing Guide - Handle late-arriving data

Contributing

See docs/installation.md#developer-installation to get started.

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License

Apache-2.0