lakelogic 1.15.0

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

LakeLogic

Your Data Estate. Under Contract.

A declarative, contract-driven medallion pipeline engine for data mesh architectures.

Describe your data products in YAML — LakeLogic materializes them as Delta/Iceberg tables with lineage, quality, and SCD2 built in.

Write once. Run on Spark, Polars, or DuckDB. The vendor-neutral alternative to Databricks Lakeflow Pipelines.

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Data Mesh Alignment

LakeLogic is the missing runtime layer for Data Mesh — where domain ownership and federated governance stop being principles and start being enforced.

Pillar	How LakeLogic Delivers
Domain Ownership	Contracts are owned and defined by domain teams (e.g., CRM, Finance) who know the data best.
Data as a Product	The contract IS the product interface — a versioned, schema-enforced, SLA-backed guarantee that consuming teams can depend on.
Self-Serve Platform	A standardized runtime that any team can use to deploy quality gates without infra silos.
Federated Governance	PII masking rules, SLA thresholds, and schema standards defined once in a central registry — automatically enforced at every domain pipeline.

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Quick Start

pip install lakelogic

from lakelogic import DataProcessor

result = DataProcessor("contract.yaml").run_source()
print(f"Valid: {result.good_count}  |  Quarantined: {result.bad_count}")

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Technical Capabilities

Data Quality & Trust

• 100% Reconciliation — Mathematically guaranteed: source = good + bad. Every row is accounted for — nothing silently dropped
• Pydantic-Powered Validation — Every contract, system & domain configs are parsed through strict Pydantic models with Literal type enforcement — invalid YAML is caught at load time, not at runtime
• SQL-First Rules — Define business logic in the language your team already speaks — no SDK, no custom DSL
• SLO Monitoring & Anomaly Detection — Native freshness, row count, and statistical anomaly detection with automatic multi-channel alerting when thresholds breach

✏️ Try it out in Google Colab: Data Quality & Trust

Compliance & Governance

• GDPR & HIPAA Compliance — Contract-driven forget_subjects() with nullify, hash, or redact strategies and immutable audit trail
• Automatic Lineage — Every row stamped with Run IDs and source paths — traceable from landing zone to Gold layer
• Pipeline Cost Intelligence — Per-entity compute cost attribution with domain-level budget governance, autoscaling-aware estimation, and Databricks Unity Catalog billing integration

✏️ Try it out in Google Colab: Compliance & Governance

Engine & Scale

• Engine Agnostic — Write once, run on Spark, Polars, or DuckDB — same contract, zero code changes
• Dimensional Modeling — Native SCD Type 2 (slowly changing dimensions), merge/upsert (SCD1), append-only fact tables, periodic snapshot overwrites, and partition-aware writes — all declared in YAML, no manual MERGE INTO SQL required
• Incremental-First — Built-in watermarking, CDC, and file-mtime tracking
• Parallel Processing — Concurrent multi-contract execution with data-layer-aware orchestration and topological dependency ordering
• Backfill & Reprocessing — Targeted late-arriving data reprocessing with partition-aware filters — no full reload required
• External Logic — Plug in custom Python scripts or notebooks for complex Gold-layer transformations while preserving full contract validation and lineage
• Production Resilience — Built-in exponential-backoff retries, per-entity timeouts, and circuit-breaker thresholds (max_consecutive_failures) — pipelines self-heal transient failures without operator intervention

✏️ Try it out in Google Colab: Engine & Scale

Developer Experience

• Structured Diagnostics & Observability — Deep contextual logging out-of-the-box (powered by loguru) featuring precise timestamps, severity levels, exact function paths, and execution tags to drastically cut troubleshooting time
• Dry Run Mode — Validate contracts, resolve dependencies, and preview execution plans without touching any data
• DDL-Only Mode — Generate and apply schema DDL (CREATE/ALTER) from contracts without running the pipeline — perfect for CI/CD migrations
• DAG Dependency Viewer — Visualize cross-contract lineage and execution order before running — understand your pipeline graph at a glance
• Data Reset & Reload — Surgically reset and reload specific entities or data layers (Bronze/Silver/Gold) without impacting the rest of the lakehouse
• Multi-Channel Alerts — Powered by Apprise for Slack, Email (SMTP/SendGrid), Teams, and Webhook notifications with ownership-based auto-routing and full Jinja2 templating support for custom formatting

✏️ Try it out in Google Colab: Developer Experience

Data Generation & AI

• Synthetic Data — Built-in DataGenerator (powered by Faker) with streaming simulation, time-windowed output, referential integrity, and AI-powered edge case injection — generate realistic error rows (SQL injection, type confusion, boundary values) for stress testing and quarantine validation
• AI Contract Onboarding — lakelogic infer auto-generates contracts from sample data with LLM-powered enrichment: automatic PII detection, column labelling, and quality rule suggestions
• Unstructured Processing — LLM extraction from PDFs, images, audio with same contract validation + lineage
• Automated Run Logs — Every pipeline run emits structured JSON with row counts, quality scores, durations, and error details — queryable as a Delta table

✏️ Try it out in Google Colab: Data Generation & AI

Integrations

• dbt Adapter — Import dbt schema.yml models and sources as LakeLogic contracts — reuse existing dbt definitions without rewriting
• dlt (Data Load Tool) — Native DltAdapter supporting 100+ verified sources (Stripe, Shopify, SQL databases, Google Analytics, and more) plus declarative REST API ingestion — all with contract-driven quality gates on arrival

✏️ Try it out in Google Colab: Integrations

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What a Contract Looks Like

One YAML file replaces hundreds of lines of validation code:

version: "1.0"
info:
  title: "Silver Customers"
  domain: "CRM"
  system: "Salesforce"

model:
  fields:
    - name: customer_id
      type: integer
      required: true
    - name: email
      type: string
      pii: true
      masking: "hash"
    - name: status
      type: string

transformations:
  - deduplicate: [customer_id]
  - sql: "SELECT *, UPPER(status) AS status_norm FROM source"
    phase: pre

quality:
  row_rules:
    - sql: "email LIKE '%@%.%'"
    - sql: "status IN ('active', 'churned', 'pending')"
  dataset_rules:
    - unique: customer_id

materialization:
  strategy: merge
  merge_keys: [customer_id]
  format: delta

Same contract, any engine — swap engine="polars" for "spark" or "duckdb". Zero code changes.

Analogy: A contract is like a building inspection checklist. The inspector (LakeLogic) checks every room (row) against the blueprint (schema), flags violations (quarantine), and stamps a certificate (lineage) — regardless of whether the building was constructed with bricks (Spark), timber (Polars), or prefab (DuckDB).

What this buys you

Without LakeLogic	With LakeLogic
500+ lines of PySpark/Pandas validation per table	40 lines of YAML
Bad rows silently dropped or crash the pipeline	Bad rows quarantined with error reasons
Schema drift discovered in production dashboards	Schema drift caught at ingestion
Manual dedup scripts per team	deduplicate: [key] — one line
PII scattered across notebooks	pii: true, masking: hash — automatic
No audit trail	Every row stamped with run ID, source path, timestamp

[!TIP] View the Complete Contract Reference for every available configuration option.

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Architecture

LakeLogic enforces Data Contracts as quality gates across the Medallion Architecture (Bronze → Silver → Gold).

Each layer uses its own contract:

Layer	Role	Guarantee
Bronze	Capture everything raw, no validation	Immutable record of source
Silver	Full validation, business rules, dedup	Trusted, queryable data
Gold	Aggregations, KPIs, ML features	Analytics-ready datasets
Quarantine	Failed rows isolated with error reasons	Nothing silently dropped

Key Guarantee: source_count = good_count + bad_count — 100% reconciliation, always.

Examples

For a complete list of runnable guides and end-to-end notebooks, please visit the Examples section of our Documentation.

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Documentation

For full guides, API references, tutorials, and contract templates, please visit the LakeLogic Documentation Site.

Contributing

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

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License

Apache-2.0