Tutorial: How to ship AI/BI Dashboard changes safely at scale with Databricks Asset Bundles

Summary

This article outlines a structured workflow for deploying AI/BI dashboards using Databricks Asset Bundles, emphasizing the importance of treating dashboards as production-grade data products. It discusses the necessity of version control, environment-specific configurations, and a controlled deployment process to ensure that dashboard changes are visible, reviewable, and reversible. The tutorial walks through the steps of adding a dashboard to an Asset Bundle, updating it while maintaining production integrity, reviewing changes through pull requests, and deploying the updated dashboard to production, all while ensuring that the changes are traceable and manageable.

Key Learnings

• 1Implementing version control for dashboards ensures that changes can be tracked and rolled back if necessary.
• 2Using Databricks Asset Bundles allows for environment-specific configurations, making it easier to manage deployments across different stages.
• 3Establishing a review process for dashboard changes promotes collaboration and reduces the risk of errors in production.
• 4Automating deployment processes can help maintain the integrity of production dashboards while allowing for iterative improvements.
• 5Understanding the history of dashboard changes is crucial for accountability and for making informed decisions based on past metrics.

Who Should Read This

Senior Data Engineers and Analytics Leads looking to implement robust version control and deployment strategies for AI/BI dashboards in Databricks.

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