lakelogic 0.13.0

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

LakeLogic

Your data pipeline breaks silently. LakeLogic catches it.

One YAML contract. Any engine. Every row validated, quarantined, or promoted — automatically.

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The Problem

You write quality checks in Spark. Then you need to run locally with Polars. Now you're maintaining two codebases. Your bronze layer has no validation. Your silver layer silently drops rows. Nobody knows which records failed or why.

The Solution

# contract.yaml — this is your entire quality gate
version: "1.0"
info:
  title: Silver Customers
  owner: data-team
model:
  fields:
    - name: customer_id
      type: integer
      required: true
    - name: email
      type: string
    - name: revenue
      type: float
    - name: status
      type: string
source:
  type: landing
  path: "data/customers/*.csv"
  load_mode: incremental
quality:
  row_rules:
    - sql: "customer_id IS NOT NULL AND email IS NOT NULL"
    - sql: "status IN ('active', 'churned', 'pending')"
    - sql: "revenue >= 0"
    - sql: "email LIKE '%@%.%'"
materialization:
  strategy: merge
  target_path: "silver/customers"
  format: parquet
  merge_keys: [customer_id]
quarantine:
  enabled: true
  target: "quarantine/customers"

from lakelogic import DataProcessor

result = DataProcessor("contract.yaml").run_source()

print(f"✅ Valid: {len(result.good)}  |  ❌ Quarantined: {len(result.bad)}")

Same contract runs on Polars, Spark, DuckDB, or Pandas. Zero code changes.

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Install

pip install lakelogic                    # Core + Polars
pip install "lakelogic[spark]"           # + PySpark
pip install "lakelogic[delta]"           # + Delta Lake (Spark-free)
pip install "lakelogic[notifications]"   # + Apprise + Jinja2 alerts
pip install "lakelogic[all]"             # Everything

What You Get

🔒 Schema & Quality Gate

Define fields, types, required constraints, and SQL-based rules in YAML. Bad rows are quarantined with tagged error reasons — never silently dropped.

🔄 Engine Portability

One contract, four engines. Develop locally on Polars in milliseconds. Deploy to Spark at scale. Same validation semantics everywhere.

📊 Declarative Transformations

Rename, derive, deduplicate, pivot, unpivot, bucket, join, filter, JSON extract, date range explode — all in YAML, all engine-agnostic.

🔗 Automatic Lineage

Every row is stamped with _lakelogic_source, _lakelogic_processed_at, and _lakelogic_run_id. Upstream lineage columns are preserved with _upstream_* prefix across layers.

📦 Incremental Processing

Watermark-based incremental loads, file-mtime tracking, run logs, and CDC support. Process only what's new.

🔔 Notifications

Slack, Teams, Email, Discord, and 90+ channels via Apprise. Built-in Jinja2 templates per event. Just add a target URL.

🏗️ Materialization

Write validated data to CSV, Parquet, Delta Lake, or Unity Catalog tables. Supports append, overwrite, merge, and SCD2 strategies.

🧪 Synthetic Data

Generate realistic test data from any contract: lakelogic generate --contract contract.yaml --rows 1000

🔌 dbt Import

Already using dbt? Convert your schema.yml in one command: lakelogic import-dbt --schema models/schema.yml --output contracts/

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Quick Start (5 Minutes)

1. Bootstrap a contract from your data

lakelogic bootstrap --landing data/ --output contracts/

This scans your files, infers schemas, detects PII, and generates ready-to-use contracts.

2. Run the quality gate

lakelogic run --contract contracts/customers.yaml --source data/customers.csv

3. See the results

✅ Good records: 847 → output/customers_good.parquet
❌ Quarantined:  23  → output/customers_quarantine.parquet
📊 Quality score: 97.4%

4. Check your environment

lakelogic doctor

LakeLogic Doctor
═══════════════════════════════════════
  Version     : 0.2.0
  Python      : 3.11.7
  OS          : Windows 11

  Engines
  ───────
  ✅ polars    1.18.0
  ✅ duckdb    1.1.3
  ✅ pandas    2.2.1
  ⬚  pyspark  not installed

  Extras
  ──────
  ✅ deltalake  0.22.3
  ✅ jinja2     3.1.4
  ✅ apprise    1.9.0
  ⬚  dataprofiler  not installed
═══════════════════════════════════════

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Architecture

┌──────────────────────────────────────────────────────────────────┐
│                         Contract YAML                           │
│  schema · SQL quality rules · transforms · lineage · target     │
└────────────────────────────┬─────────────────────────────────────┘
                             │
                      ┌──────▼──────┐
                      │ DataProcessor│
                      └──────┬──────┘
                             │
        ┌────────────┬───────┼───────┬────────────┐
        ▼            ▼       ▼       ▼            │
   ┌────────┐  ┌────────┐ ┌───────┐ ┌────────┐   │
   │ Polars │  │ Spark  │ │DuckDB │ │ Pandas │   │
   └───┬────┘  └───┬────┘ └──┬────┘ └───┬────┘   │
       │           │         │          │         │
       └───────────┴────┬────┴──────────┘         │
                        │                         │
               ┌────────▼────────┐                │
               │  Validated Data  │                │
               │  ┌────┐ ┌─────┐ │                │
               │  │Good│ │ Bad │ │                │
               │  └──┬─┘ └──┬──┘ │                │
               └─────┼──────┼────┘                │
                     │      │                     │
               ┌─────▼┐  ┌──▼────────┐            │
               │Target│  │Quarantine │            │
               └──────┘  └───────────┘            │

Explore the Examples

The examples/ directory contains runnable notebooks across three learning tracks:

Folder	What You'll Learn
01_quickstart/	Remote CSV ingestion, database governance, dbt + PII quality
02_core_patterns/	Bronze quality gate, medallion architecture, SCD2, deduplication, reference joins, soft deletes
03_compliance_governance/	HIPAA & GDPR Policy Packs, automated PII masking, audit-ready quarantine

Documentation

• Full Docs — Complete guides and API reference
• Quickstart — Get running in 5 minutes
• Contract Reference — Full YAML field reference
• CLI Reference — Command-line usage

Contributing

See CONTRIBUTING.md to get started, or docs/installation.md#developer-installation for environment setup.

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License

Apache-2.0