Over the last few days, I explored a practical workflow using Codex CLI together with Databricks CLI to manage Databricks operations directly from the terminal.
♟️ Instead of manually navigating the UI, I used Codex as an engineering/platform assistant on top of an authenticated Databricks CLI session to:
• Inspect jobs and pipelines
• Deploy notebooks into the Databricks workspace
• Create and update Databricks jobs
• Generate scheduled governance reports
• Automate checks for untagged artifacts
♟️ What stood out to me is how effective this combination can be for engineering productivity:
• Codex CLI helps reason through the task, write the code, and orchestrate the workflow
• Databricks CLI provides direct control over workspace resources
• Together they enable a fast, repeatable, terminal-first way of working with Databricks
♟️ This pattern can be useful for:
• Job automation
• Notebook deployment
• Metadata and governance audits
• Reporting workflows
• Operational support for Databricks environments
🔬 The broader takeaway is simple:
"AI-assisted CLI workflows are becoming a practical way to manage real platform engineering tasks, not just generate code"
If you are already using Databricks CLI, pairing it with Codex CLI can significantly reduce manual effort for workspace operations and automation use cases.
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