Limitations
Learn about the Cloudera Data Engineering limitations.
- Tier 2 node groups cannot be edited through the UI or API
- Once a Tier 2 node group is created as part of service creation, the Tier 2 node group cannot be edited.
- No support for Data Lake resizing
- Cloudera Data Engineering does not support Data Lake resizing.
- Running raw Scala code in Cloudera Data Engineering
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- When setting the Log Level from the user interface, the setting is not applied to the raw Scala jobs.
- Do not use package <something> in the raw Scala job file as Raw Scala File is used for Scripting and not for Jar development and packaging.
- Spark job schedule or configuration changes modify the job owner
- Any modifications to the schedule or the configuration of a Spark job in Cloudera Data Engineering changes the job owner to the user who last edited it. This
behavior can lead to access issues, as it affects job run permissions.
You can assign the job ownership to a service account, but you cannot log in with a service account through the Cloudera Data Engineering UI. As a workaround, use the CDE CLI with the service account to edit the Spark job schedule or configuration and to reset the job ownership.
- Usernames on Cloudera Data Engineering virtual clusters
- Cloudera Data Engineering virtual clusters require lowercase usernames.
- Cloudera Data Engineering API for Airflow Operators and Libraries
- The Cloudera Data Engineering API for Airflow Operators and Libraries currently expects Base64-encoded certificates, but the Cloudera Data Engineering UI sends PEM certificates without conversion. As a workaround, go to and provide the certificate in Base64 format.
