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后端与数据库

databricks-jobs

databricks/data…t-skills

Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI. Use when creating data engineering jobs with notebooks, Python wheels, SQL, dbt, or pipelines. Invoke BEFORE starting implementation. For compute (serverless, performance mode, classic), also use databricks-serverless.

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安装命令

npx skills add https://github.com/databricks/databricks-agent-skills --skill databricks-jobs

SKILL.md

Lakeflow Jobs Development

FIRST: Use the parent databricks-core skill for CLI basics, authentication, profile selection, and data exploration commands.

Compute: jobs run on serverless by default. Before you choose compute or write any cluster setting, use the databricks-serverless skill; it decides serverless or classic and the performance mode.

Lakeflow Jobs orchestrate data workflows with multi-task DAGs, flexible triggers, and comprehensive monitoring. Jobs support diverse task types and can be managed via Declarative Automation 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

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