Troubleshooting & FAQ: Databricks

What Databricks platforms are supported?
All three Databricks platforms (AWS, GCP and Azure) are supported!

What about Delta Lake?
Delta Tables are supported and delta size and freshness metrics are monitored with automated monitors. You can also opt in to any field health, dimension, custom SQL and SLI monitors as well. See here for additional details.

What about my non Delta tables?
Like with Delta tables you can opt into field health, dimension, custom SQL and SLI monitors. To enable write throughput and freshness please enable S3 metadata events. See here for details on how to set up this integration.

How many Databricks workspaces are supported?
We can support multiple workspaces by setting up additional integrations. If you are using the unity catalog, you only need to set up a single connection to that catalog, even if there are multiple workspaces connected to it.

Are there any limitations?
Freshness SLOs with Glue or an external Hive Metastore are not supported.
Non-delta tables require S3 Events - Metadata.

Can I use another Query Engine instead of Databricks SQL?
Yes, you can use any of the supported Query Engines under the Data Lakes documentation.

What is the minimum Data Collector version required?
The Databricks integration requires at least v3071 of the Data Collector. You can use the Monte Carlo CLI to verify the current version of your Data Collector and upgrade if necessary.

$ montecarlo collectors list

If the Data Collector you are using is out-of-date you will see a "Databricks operations require DC v3071 or above" error during onboarding.

How do I handle a "Cannot make databricks job request for a DC with disabled remote updates" error?
If you have disabled remote updates on your Data Collector we cannot automatically provision resources in your Databricks workspace using the CLI. Please reach out to your account representative for details on how to create these resources manually.

How do I handle a "A Databricks connection already exists" error?
This means you have already connected to Databricks. You cannot have more than one Databricks metastore or Databricks delta integration.

How do I handle a "Scope monte-carlo-collector-gateway-scope already exists" error?
This means a scope with this name already exists in your workspace. You can specify creating a scope with a different name using the --databricks-secret-scope flag.

Alternatively, after carefully reviewing usage, you can delete the scope via the Databricks CLI/API. Please ensure you are not using this scope elsewhere as any secrets attached to the scope are not recoverable. See details here.

How do I handle a "Path (/monte_carlo/collector/integrations/collection.py) already exists" error?
This means a notebook with this name already exists in your workspace. If you can confirm this is a notebook provisioned by Monte Carlo and there are no existing jobs you should be able to delete the notebook via the Databricks CLI/API. See details here. Otherwise please reach out to your account representative.

How do I retrieve job logs?

  1. Open your Databricks workspace.
  2. Select Workflows from the sidebar menu.
  3. Select Jobs from the top and search for a job containing the name monte-carlo-metadata-collection.
  4. Select the job.
  5. Select any run to review logs for that particular execution. The jobs should all show Succeeded for the status, but for partial failures (e.g. S3 permission issues) the log output will contain the errors and overall error counts.