{"id":35196797,"url":"https://github.com/elevata-labs/elevata","last_synced_at":"2026-05-25T11:02:45.280Z","repository":{"id":319642762,"uuid":"1070570972","full_name":"elevata-labs/elevata","owner":"elevata-labs","description":"elevata is an Architecture Runtime for modern data platforms — metadata-native, warehouse-agnostic, and deterministic by design.","archived":false,"fork":false,"pushed_at":"2026-05-18T04:44:42.000Z","size":38367,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2026-05-18T06:45:59.310Z","etag":null,"topics":["analytics-engineering","architecture-first","architecture-runtime","data-architecture","data-engineering","data-modeling","data-platform","elevata","lakehouse","metadata-driven","metadata-management","modern-data-stack","open-source","platform-agnostic","sql","warehouse-agnostic"],"latest_commit_sha":null,"homepage":"https://elevata-labs.github.io/elevata/","language":"Python","has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/elevata-labs.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":"NOTICE.md","maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-10-06T06:12:23.000Z","updated_at":"2026-05-18T04:42:32.000Z","dependencies_parsed_at":null,"dependency_job_id":"6647f016-dcbe-40f6-885b-efaaaea9f550","html_url":"https://github.com/elevata-labs/elevata","commit_stats":null,"previous_names":["elevata-labs/elevata"],"tags_count":36,"template":false,"template_full_name":null,"purl":"pkg:github/elevata-labs/elevata","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/elevata-labs%2Felevata","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/elevata-labs%2Felevata/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/elevata-labs%2Felevata/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/elevata-labs%2Felevata/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/elevata-labs","download_url":"https://codeload.github.com/elevata-labs/elevata/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/elevata-labs%2Felevata/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33471530,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-25T06:32:55.349Z","status":"ssl_error","status_checked_at":"2026-05-25T06:32:35.322Z","response_time":57,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analytics-engineering","architecture-first","architecture-runtime","data-architecture","data-engineering","data-modeling","data-platform","elevata","lakehouse","metadata-driven","metadata-management","modern-data-stack","open-source","platform-agnostic","sql","warehouse-agnostic"],"created_at":"2025-12-29T07:39:55.161Z","updated_at":"2026-05-25T11:02:45.272Z","avatar_url":"https://github.com/elevata-labs.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# elevata®\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://raw.githubusercontent.com/elevata-labs/elevata/main/docs/elevata_logo.png\" alt=\"elevata logo\" width=\"130\"/\u003e\n\u003c/p\u003e\n\n**elevata® is an Architecture Runtime for modern data platforms.**\n\nIt turns **metadata into deterministic, executable data architecture**.\nArchitecture is defined declaratively and executed consistently across warehouses.\n\nSQL becomes an artifact. Architecture becomes metadata.\n\n---\n\n## ⚡ What elevata enables\n\nThe same metadata-defined platform can run consistently on:\n\nSnowflake · Databricks · Fabric · MSSQL · Postgres · DuckDB · BigQuery\n\nwithout rewriting logic or introducing dialect-specific modeling.\n\nelevata separates:\n\n- **Logical architecture**  \n- **Dialect rendering**  \n- **Execution backend**\n\nThis makes data architecture portable, reproducible, and governable.\n\n## License \u0026 Dependencies\n\n[![License: AGPL v3](https://img.shields.io/badge/License-AGPL_v3-blue.svg)](https://github.com/elevata-labs/elevata/blob/main/LICENSE)\n[![Built with Django](https://img.shields.io/badge/Built%20with-Django-092E20?logo=django)](https://www.djangoproject.com/)\n[![Frontend: HTMX](https://img.shields.io/badge/Frontend-HTMX-3366CC?logo=htmx)](https://htmx.org/)\n[![UI: Bootstrap 5](https://img.shields.io/badge/UI-Bootstrap%205-7952B3?logo=bootstrap)](https://getbootstrap.com/)  \n\n---\n\n## 🧭 What is elevata?\n\nelevata is a **metadata-first** data platform engine.\n\nIt models datasets, lineage, governance, and execution semantics declaratively.\n\nFrom these definitions, elevata derives deterministic logical plans, renders dialect-owned SQL,  \nand executes warehouse-native pipelines.\n\nSchema evolution, incremental loads and historization are planned,  \nvalidated, and applied deterministically before execution.\n\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://raw.githubusercontent.com/elevata-labs/elevata/main/docs/elevata_v2_0_0.png\" alt=\"elevata UI preview\" width=\"700\"/\u003e\n  \u003cbr/\u003e\n  \u003cem\u003eDataset detail view with lineage, metadata, and dialect-aware SQL previews\u003c/em\u003e\n\u003c/p\u003e\n\n\n## ✨ Why elevata is different\n\nMost data platforms encode architecture implicitly in SQL and pipeline code.\n\nelevata makes architecture explicit.\n\n- Metadata defines behavior.  \n- Dialects own SQL shape.  \n- Execution is deterministic and observable.\n\nThe result is governed, explainable, and portable data architecture.\n\n---\n\nelevata models datasets, lineage, keys, and execution semantics declaratively.\n\nFrom this metadata, it derives deterministic logical plans and renders dialect-owned SQL.\n\nThe same architecture executes across supported warehouses without changing dataset definitions.\n\n\u003e *Modern data platforms often fail not because of missing tools, but because*  \n\u003e *architecture, lineage, and governance are encoded implicitly in SQL and pipeline code.*  \n\u003e *elevata exists to make these concerns explicit, declarative, and reproducible.*\n\n---\n\n## 🧩 Architecture Overview\n\nelevata consists of four layers:\n\n1. **Metadata Model**  \n2. **Deterministic Logical Plan**  \n3. **Dialect Rendering**  \n4. **Warehouse-Native Execution**\n\nEach layer is explicitly separated.\n\n---\n\n## 📚 Example Workflow\n\n1. Define datasets and lineage in metadata  \n2. Inspect generated SQL and lineage  \n3. Execute pipelines deterministically on your target warehouse\n\n---\n\n## 💻 Execution\n\nPipelines are executed dataset-driven and lineage-aware.\n\nExecution supports full and incremental loads, historization,  \nschema evolution, and structured load logging.\n\nBehavior is deterministic and observable.\n\nSchema drift is reconciled through Architecture MigrationPlan-driven materialization:  \nrenames, adds, type evolution and controlled rebuilds are derived from architecture state,  \nwhile destructive changes remain explicitly policy-gated.\n\n---\n\n## 🧭 Architecture Control\n\nelevata makes architecture changes reviewable before execution.\n\nArchitecture State, Change Reports, Promotion Reports, Approval Artifacts and Execution Records  \nexpose deterministic fingerprints, MigrationPlan actions, policy decisions, review decisions and execution outcomes.\n\nThis supports controlled review, CI checks and environment-to-environment architecture promotion  \nwhile keeping execution guardrails inside the load runner.\n\nThe Architecture Control UI makes approval state, scope, policy status, change summary,  \nexecution preview, dependency mode, captured output and execution records visible for controlled scopes.\n\nUsers can inspect reports, download report JSON, create Approval Artifacts, verify approvals,  \nexecute approved or no-change scopes, and inspect the resulting Architecture Execution Record.\n\n---\n\n## 📐 Query Builder\n\nelevata models transformations explicitly using **Query Trees**.\n\nEach TargetDataset may define a query tree composed of well-defined  \noperators such as SELECT, JOIN, AGGREGATE, UNION and WINDOW.  \nThese operators are represented as metadata objects, not as opaque SQL fragments.\n\nThe Query Builder models transformations explicitly using structured metadata.\n\nIt produces deterministic SQL with stable contracts and field-level lineage.\n\n---\n\n## 🔮 Roadmap\n\nelevata evolves along three strategic axes:\n\n**1. Ingestion \u0026 Source Abstraction**  \nExpanding source patterns (files, APIs, cloud transports)  \nwhile preserving deterministic RAW semantics.\n\n**2. Metadata Governance \u0026 Contracts**  \nVersioning, breaking-change detection, lineage validation  \nand reproducible execution snapshots.\n\n**3. Performance \u0026 Adaptive Execution**  \nWarehouse-specific optimization layers and adaptive materialization strategies.\n\nSee `/docs` for architectural depth.\n\n---\n\n### ♟️ Architecture \u0026 Strategy\n\nFor a deeper architectural and strategic overview of elevata’s direction,\nsee the [elevata Platform Strategy](https://github.com/elevata-labs/elevata/blob/main/docs/strategy/elevata_platform_strategy.md).\n\n---\n\n## 🛡️ Data Privacy (GDPR/DSGVO)\n\nelevata itself does not require personal data.  \nIf used with customer datasets, responsibility for compliance remains with the implementing organisation.  \nThe system supports pseudo-key hashing and consistent anonymisation strategies via its hashing DSL.\n\n---\n\n## Disclaimer\n\nThis project is an independent open-source initiative.  \n- It is not a consulting service.  \n- It is not a customer project.  \n- It does not store or process customer data.  \n- It is not in competition with any company.  \n\nThe purpose of elevata is to contribute to the community by providing a metadata-centric framework for building data platforms.  \nThe project is published under the AGPL v3 license and open for use by any organization.\n\n---\n\n## 🧾 License \u0026 Trademark Notice\n\n© 2025-2026 Ilona Tag — All rights reserved.  \n**elevata®** is an open-source software project for data \u0026 analytics innovation.  \n\nelevata® is a registered trademark in Germany.  \nOther product names, logos, and brands mentioned here are property of their respective owners.\n\nReleased under the **GNU Affero General Public License v3 (AGPL-3.0)**.  \nSee [`LICENSE`](https://github.com/elevata-labs/elevata/blob/main/LICENSE) for terms and [`NOTICE.md`](https://github.com/elevata-labs/elevata/blob/main/NOTICE.md) for third-party license information.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Felevata-labs%2Felevata","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Felevata-labs%2Felevata","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Felevata-labs%2Felevata/lists"}