delta-engine 0.1.0

Framework for defining, validating, and evolving tables in a consistent, safe, and controlled way.

delta-engine

Declarative schema management for Delta Lake tables on Databricks. Define the schema you want; the engine plans, validates, and applies the DDL.

Quickstart

from delta_engine.databricks import build_engine
from delta_engine.schema import Column, DeltaTable, Integer, String

customers = DeltaTable(
    catalog="dev",
    schema="silver",
    name="customers",
    columns=[
        Column("id", Integer()),
        Column("name", String()),
    ],
)

engine = build_engine(spark)
engine.sync(customers)  # creates the table, or no-ops if it already matches

How-to guides

• Getting started — define a table and run your first sync
• Handle sync failures — inspect SyncReport and act on each status
• Configure table properties — Property enum, defaults, overrides

Reference

• Data types — supported types and Spark SQL equivalents
• Safe-change rules — changes the engine blocks at validation
• API reference — full public API

Developer

• Architecture — layers, design decisions, why hexagonal
• Implement a custom adapter — CatalogStateReader and PlanExecutor protocols
• Add a new action type — extend Action, ActionPhase, and the compiler

Contributing

Commit messages and PR titles follow Conventional Commits; the PR title drives the automated version bump and changelog. To lint your commit messages locally, run this once after cloning:

uv run pre-commit install --hook-type commit-msg