koza 2.6.2

Data transformation framework for LinkML data models

Koza - Knowledge Graph Transformation and Operations Toolkit

Documentation

Overview

Koza is a Python library and CLI tool for transforming biomedical data and performing graph operations on Knowledge Graph Exchange (KGX) files. It provides two main capabilities:

📊 Graph Operations (New!)

Powerful DuckDB-based operations for KGX knowledge graphs:

• Join multiple KGX files with schema harmonization
• Split files by field values with format conversion
• Prune dangling edges and handle singleton nodes
• Append new data to existing databases with schema evolution
• Multi-format support for TSV, JSONL, and Parquet files

🔄 Data Transformation (Core)

Transform biomedical data sources into KGX format:

• Transform csv, json, yaml, jsonl, and xml to target formats
• Output in KGX format
• Write data transforms in semi-declarative Python
• Configure source files, columns/properties, and metadata in YAML
• Create mapping files and translation tables between vocabularies

Installation

Koza is available on PyPi and can be installed via pip/pipx:

[pip|pipx] install koza

Usage

See the Koza documentation for complete usage information.

Key Features

🔧 Multi-Format Support

• Native support for TSV, JSONL, and Parquet KGX files
• Automatic format detection and conversion
• Mixed-format operations in single commands

🛡️ Schema Flexibility

• Automatic schema harmonization across heterogeneous files
• Schema evolution with backward compatibility
• Comprehensive schema reporting and validation

⚡ High Performance

• DuckDB-powered operations for fast bulk processing
• Memory-efficient handling of large knowledge graphs
• Parallel processing and streaming where possible

🔍 Rich CLI Experience

• Progress indicators for long-running operations
• Detailed statistics and operation summaries
• Dry-run modes for safe operation preview

🧹 Data Integrity

• Dangling edge detection and preservation
• Duplicate detection and removal strategies
• Non-destructive operations with data archiving