# Changelog

All notable changes to **LakeLogic** are documented here.

Format follows [Keep a Changelog](https://keepachangelog.com/en/1.1.0/).
Versions follow [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

---
## [1.5.0] — 2026-03-24

### Added

- Add GitHub Actions CI quality gate for Python linting and tests.
- Add GitHub Actions CI quality gate for backend linting and core tests with coverage checks.
- Add quickstart and pipeline examples including notebooks and associated data contracts for bronze, silver, and gold layers.
## [1.4.0] — 2026-03-24

### Added

- Implement core data contract inference, generation, and pipeline execution framework with multi-engine support, removing temporary development files.
- Enhance data contract specification with schema policies, reference data, and soft deletes, and add new documentation and a quickstart example.
- Introduce core LakeLogic data processing, validation, and materialization features with extensive documentation and architecture diagrams.
- Implement initial declarative data mesh pipeline engine with core processing, AI data generation, and GDPR compliance modules.

### Documentation

- Update README to detail LakeLogic's alignment with Data Mesh pillars.
- Add initial project documentation and enhance the README contract example with clarifications and new service level objectives.
- Remove "The Problem" section and "The Solution" header from README.
## [1.3.0] — 2026-03-18

### Added

- Add comprehensive project documentation, custom styling, and core observer and schema API modules.
- Add Polars execution engine adapter with SQL transformations, link registration, and schema application.
- Introduce core data processing logic with engine adapters and dbt integration.
- Add documentation for engine capabilities, lakehouse catalog table name resolution, and a contract template.
- Add documentation for engine capabilities, lakehouse catalog table name resolution, and a contract template.
- Add LLM extraction engine and model registry for unstructured data processing
- Enhance schema validation and add GenericSQL adapter
- Add core materialization utilities for data persistence, including Spark table management, path resolution, and dataframe output.

### Changed

- Clean up import statements and exception handling in GenericSQLAdapter
## [1.0.0] — 2026-03-05

### Added

- Add Spark engine adapter for data contract execution and transformation.
- Add Spark engine row-level validation and engine-agnostic row counts, implement automated changelog and release workflows, and fix Spark `.isFalse()` bug and CHANGELOG structure.
- Implement core processor for remote data ingestion, add quickstart examples, and set up automated changelog generation.
- Introduce Spark execution engine for data contract validation and transformation.
- Add "Hello World" example notebook for remote data ingestion and quality validation, and update the changelog.
## [0.11.0] — 2026-03-03

### Added

- Add PII masking hook with direct and NLP-based replacement modes, and refine changelog documentation.
- Add tutorials for HIPAA, GDPR compliance, and PII masking.
- Add tutorials demonstrating HIPAA/GDPR compliance and PII masking.
- Introduce the foundational LakeLogic data processing framework, including core modules, engine integrations, AI, and CLI.
- Add script to synchronize flagship examples to the documentation directory.
- Add a script to synchronize flagship examples to the documentation and update MkDocs navigation to feature new interactive examples.
- Add new core materialization module, comprehensive examples, and initial documentation, while updating the main README.
- Add 01_hello_world.ipynb quickstart example for remote data ingestion.
- Add quickstart examples for remote data ingestion, database governance, and dbt PII quality, supported by new core materialization and models.
- Add BigQuery and Snowflake engine adapters along with a dependency management utility for optional packages.

### Documentation

- Add documentation index page
## [0.7.0] — 2026-02-28

### Added

- Add Polars engine, new examples for HIPAA/GDPR compliance and AI contract enrichment, and installation documentation.
## [0.6.0] — 2026-02-28

### Added

- Add CI quality gate and PyPI publish workflows, and update documentation examples via a new sync script.
- Add extensive examples for quickstart, core patterns, advanced workflows, and compliance, alongside new documentation and a schema API.
## [0.4.0] — 2026-02-28

### Added

- Add AI-powered contract enrichment functionality with LLM provider abstraction.
## [0.3.0] — 2026-02-28

### Added

- Implement the initial command-line interface for contract execution, output management, and environment setup.
- Implement a universal notification system using Apprise with new Jinja2 templates and a base adapter.
- Add quickstart examples for data ingestion across various file formats using contracts and notebooks.
- Add new documentation for notifications & secrets, playbooks, and a main index, and update mkdocs navigation.
- Add extensive documentation including comparison, installation, and architecture guides, update project branding, and introduce core processor logic.
- Introduce new examples covering advanced workflows and compliance/governance scenarios, including data, contracts, and notebooks.
- Implement initial LakeLogic framework with contract inference, multi-engine support, and Databricks deployment configuration.
- Establish core data contract models and initial data processing infrastructure with engine support.
- Implement contract inference from data files, add dbt adapter, schema API, and advanced workflow examples.

### Documentation

- Add initial documentation index page.
- Add `docs/index.md` and correct capitalization of 'LakeLogic' in `mkdocs.yml` URLs and repository names.
## [0.1.0] — 2026-02-22

### Added

- Add extensive examples for data sources, core patterns, and advanced workflows, along with new core engine and CLI components.
- Add extensive examples for data sources, core patterns, and advanced workflows, along with new data engines and core utilities.
- Introduce new quickstart examples for remote data ingestion and database governance, add notebook cleaning utilities, and expand installation documentation.
- Implement comprehensive documentation site with custom styling and introduce a base engine adapter for data quality rule processing with updated quickstart examples.
- Add final_cleanup.py script to remove specific example files and directories.
- Introduce core data processing logic with engine auto-discovery, `ValidationResult`, and new DuckDB engine, alongside comprehensive examples for data sources and compliance.
- Add contract templating tool and a comprehensive advanced workflow example for shared governance at scale.
- Add comprehensive examples for quickstarts, core patterns, data sources, cloud platforms, orchestration, advanced workflows, and production scenarios.
- Introduce core data materialization logic and initial streaming components.
- Introduce comprehensive examples and tutorials for databases, streaming, APIs, and cloud platforms, along with new streaming implementation documentation.
- Introduce extensive new features, examples, and documentation for data integration, streaming, and cataloging, including detailed logging configuration.
- Add a comprehensive contract template reference documentation page and update mkdocs navigation.
- Implement initial Azure infrastructure with Terraform modules for dev, test, and prod environments, including CI/CD workflows and documentation.

### CI/CD

- Add GitHub Actions workflow for backend Python quality checks including linting and formatting.

### Documentation

- Add comprehensive documentation for DataProcessor return values and update mkdocs navigation.
- Add architecture diagram documentation and ignore the `.product_vision` directory.
## [0.1.0b1] — 2026-02-08

### Added

- Establish initial project structure with core logic, CLI, multiple engines, comprehensive documentation, examples, and a test suite.
- Introduce comprehensive documentation, examples, and support for multiple data engines, core logic, and CLI functionalities.
- Add PyPI publishing workflow and comprehensive MkDocs documentation site
- Add GitHub Actions workflow for publishing to PyPI.
- Add basic validation contract for the `silver_crm_customers` dataset example.
- Introduce core Lakeguard framework with multiple data engines, comprehensive examples, and extensive documentation.
- Add comprehensive examples, tutorials, documentation, and new engine implementations for various data platforms.

### CI/CD

- Add GitHub Actions workflow to build and publish Python packages to PyPI.
---

<!-- Link definitions -->
[1.5.0]: https://github.com/lakelogic/LakeLogic/compare/v1.4.0...v1.5.0
[1.4.0]: https://github.com/lakelogic/LakeLogic/compare/v1.3.0...v1.4.0
[1.3.0]: https://github.com/lakelogic/LakeLogic/compare/v1.0.0...v1.3.0
[1.0.0]: https://github.com/lakelogic/LakeLogic/compare/v0.11.0...v1.0.0
[0.11.0]: https://github.com/lakelogic/LakeLogic/compare/v0.7.0...v0.11.0
[0.7.0]: https://github.com/lakelogic/LakeLogic/compare/v0.6.0...v0.7.0
[0.6.0]: https://github.com/lakelogic/LakeLogic/compare/v0.4.0...v0.6.0
[0.4.0]: https://github.com/lakelogic/LakeLogic/compare/v0.3.0...v0.4.0
[0.3.0]: https://github.com/lakelogic/LakeLogic/compare/v0.1.0...v0.3.0
[0.1.0]: https://github.com/lakelogic/LakeLogic/compare/v0.1.0b1...v0.1.0
[0.1.0b1]: https://github.com/lakelogic/LakeLogic/releases/tag/v0.1.0b1

