AltimateAI / AltimateAI/altimate-code
Add Databricks API support for notebooks, jobs, and clusters
- Lenguaje dominante
- TypeScript
- Estrellas
- 811
- Forks
- 134
- Merge medio
- 3 d 2 h
- PR fusionados (30 d)
- 50
Descripción
No Databricks APIs are currently available for managing notebooks, jobs, or clusters. Specifically requested capabilities:
- Create/manage Databricks Notebooks (Workspace REST API)
- Create/manage Databricks Jobs/Workflows (Jobs API) for scheduling and orchestration
- Create/manage Delta Live Tables (DLT) pipelines
- Manage clusters/compute (Cluster API)
Currently, altimate-code can only interact with Databricks via SQL warehouse connections (sql_execute, schema_inspect, dbt models, cost/finops analysis, lineage). There's no way to programmatically create or manage notebooks, jobs, or clusters directly — users must generate the content as text and manually apply it via the Databricks UI or CLI. Native API integration for these would let altimate-code build and orchestrate full Databricks pipelines, not just SQL-layer transformations.
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### Metadata
| Field | Value |
|-------|-------|
| CLI Version | 0.9.7 |
| Platform | darwin |
| Architecture | arm64 |
| OS Release | 25.5.0 |
| Category | feature |
Guía de contribución
Línea de trabajo
Start by surveying the existing sql_execute, schema_inspect, dbt models, cost/finops, and lineage integrations to find the CLI's current Databricks connection points. Define the API entry points and tests for notebooks, jobs, DLT pipelines, and clusters; done means these resources can be managed programmatically rather than only generated for manual UI or CLI application.
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Evaluación
- Stack tecnológico
- typescript
- Área
- backend-api-design, cli, data-engineering
- Tipo de issue
- Nueva funcionalidad
- Dificultad
- 5/5
- Tiempo estimado
- Más de una semana
- Estado de actividad
- Activo
- Claridad
- Necesita aclaración
- Aptitud para principiantes
- 30/100