Hi @trentlglover,

It sounds like you're experiencing a significant performance issue with your notebooks in the new development environment. Here are a few potential areas to investigate:

1. Cluster Configuration: Even though you mentioned that the compute configuration is the same, ensure that the cluster settings (e.g., instance types, autoscaling settings) are identical. Sometimes, subtle differences in configurations can lead to performance discrepancies.
2. Environment Differences: Check if there are any differences in the environment settings between production and development. This includes library versions, runtime versions, and any initialization scripts that might be running.
3. Data Access and Network Latency: Verify if the data sources accessed by the notebooks in the development environment are the same as in production. Network latency or differences in data access paths can significantly impact performance.
4. Resource Contention: In a development environment, there might be more resource contention if multiple users or processes are running simultaneously. Check if the cluster is being shared with other workloads that might be consuming resources.
5. Logs and Metrics: Examine the logs and metrics for the development cluster to identify any bottlenecks or errors. This can provide insights into what might be causing the delays.
6. Code Changes: Ensure that the code in the notebooks has not been altered during the migration. Even small changes can sometimes lead to unexpected performance issues.

If none of these areas reveal the cause, you might want to consider opening a case for a more in-depth investigation. Please refer to: https://docs.databricks.com/en/resources/support.html