How to Orchestrate Across Multiple Databricks Workspaces Without Losing Your Mind
Blog post from Dagster
As Databricks scales by multiplying workspaces, orchestrating data pipelines becomes a complex challenge, transforming from a single system coordination to managing a distributed one. This complexity arises from the inability to track dependencies and manage coordinated execution across multiple workspaces, leading to issues such as stale data and inconsistent outputs when upstream failures occur. The proposed solution involves enhancing visibility and coordination using Dagster, which allows for the observation, definition, and orchestration of dependencies across workspaces. By integrating tools like the DatabricksWorkspaceComponent, teams can load jobs into a single asset graph, making cross-workspace dependencies explicit and manageable. This shift from implied to declared dependencies ensures that downstream jobs do not execute on outdated inputs, improving data reliability and workflow efficiency.
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