Graph-Powered Observability Data Analysis in Databricks With Credential Vending

Observability data — logs, metrics, and traces — captures the complex interactions within modern distributed systems. A graph query engine on top of Databricks enables complex traversal of massive observability data, helping users trace service dependencies, analyze upstream/downstream impacts, and uncover recurring error patterns, making it easier to diagnose issues and optimize system performance.

A critical challenge in handling observability data is managing dynamic RBAC for the sensitive system telemetry. This session explains how Coinbase leverages credential vending, a method for issuing short-lived credentials to enable fine-grained, secure access to observability data stored in Databricks without long-lived secrets.

Key takeaways:

• Querying Databricks tables as graph structures without ETLing data out
• Secure access management with credential vending
• Practical graph-based incident analysis solution at Coinbase, with insights on how PuppyGraph enables this application