phlo 0.6.0

Lakehouse platform

Modern data lakehouse platform built on Dagster, DLT, Iceberg, Nessie, and dbt.

Features

• Decorator-driven development - Reduce boilerplate by 74% with @phlo.operations.ingestion and @phlo.quality
• Write-Audit-Publish pattern - Git-like branching with automatic quality gates and promotion
• Type-safe data quality - Pandera schemas enforce validation and generate Iceberg tables
• Plugin architecture - Extensible via service, source, quality, and transformation plugins
• Observatory UI - Web-based interface for data exploration, lineage, and monitoring
• Production-ready patterns - Auto-publishing to Postgres, configurable merge strategies, freshness policies
• Modern tooling - Built on Dagster, DLT, Iceberg, Nessie, dbt, and Trino

Prerequisites

• uv - Python package manager
• Docker - Container runtime

Quick Start

# Install with default services
uv pip install phlo[defaults]

# Initialize a new project
phlo init my-project
cd my-project

# Start services and run
phlo services start
phlo materialize --select "dlt_glucose_entries+"

Documentation

Full documentation at docs/index.md:

• Installation Guide
• Quickstart Guide
• Core Concepts
• Developer Guide
• Plugin Development
• CLI Reference
• Configuration Reference
• Operations Guide
• Blog Series - Data Engineering Fundamentals with Phlo

Development

# Services
phlo services start    # Start all services
phlo services stop     # Stop services
phlo services logs -f  # View logs

# Development
phlo services start    # Start all services
phlo services stop     # Stop services
phlo services logs -f  # View logs

# Development
uv pip install -e .    # Install Phlo
make check             # Lint, format, typecheck, and test
phlo test              # Run tests only