databricks-jobs
Originally fromdatabricks-solutions/ai-dev-kit
Installation
SKILL.md
Lakeflow Jobs Development
FIRST: Use the parent databricks-core skill for CLI basics, authentication, profile selection, and data exploration commands.
Lakeflow Jobs orchestrate data workflows with multi-task DAGs, flexible triggers, and comprehensive monitoring. Jobs support diverse task types and can be managed via Asset Bundles (DABs), Python SDK, or CLI.
Reference Files
| Use Case | Reference File |
|---|---|
| Configure task types (notebook, Python, SQL, dbt, pipeline, JAR, run_job, for_each) | references/task-types.md |
| Set up triggers and schedules (cron, periodic, file arrival, table update, continuous) | references/triggers-schedules.md |
| Configure notifications, health rules, retries, timeouts, queues | references/notifications-monitoring.md |
| Complete worked examples (ETL, warehouse refresh, event-driven, ML training, multi-env, streaming, cross-job) | references/examples.md |
Scaffolding a New Job Project
Use databricks bundle init with a config file to scaffold non-interactively. This creates a project in the <project_name>/ directory: