Assigns classic compute to one person or one group. Some capabilities work only in this mode, among them R, the RDD API, GPU instances and the ML flavour of Databricks Runtime.
Also called formerly single user access mode, Single user access mode.
Read more: Microsoft Learn
In the Ultra Transcenders books
Each book explains Dedicated access mode in context, with comparison tables and the common traps.
Terms in this definition
- Classic compute
All-purpose, jobs and pipeline compute running in your own Azure subscription, which you set up and manage yourself. Its counterpart is serverless compute, which Azure Databricks runs for you.
- RDD
Short for Resilient Distributed Dataset, the older low-level Spark interface for working with distributed collections. Serverless compute and standard access mode don't allow it, so code relying on RDDs needs dedicated compute.
- API
Short for application programming interface: a contract that client code calls programmatically, for example a web API secured with tokens or the Files, Images or Responses APIs.
- GPU
A graphics processing unit, an accelerator suited to highly parallel work like graphics and AI. Azure offers them in the NV and NC VM series.
- Databricks Runtime
The software image, Apache Spark among its core parts, that Azure Databricks installs on compute. Long-term support (LTS) releases are the advised choice when job compute runs production work.
Related terms
- Fine-grained DML privileges
Narrower grants than
MODIFY, currently in Beta, letting someone alter the data in a Unity Catalog table while its schema and other metadata stay out of reach. TheUPDATE,DELETEandINSERTprivileges require at least Databricks Runtime 18.1 and can't be used on compute in dedicated access mode. - REFRESH FOREIGN TABLE
Brings Unity Catalog's stored metadata for one foreign table up to date. Under dedicated access mode, both this statement and reading the table require
ALL PRIVILEGES. - UDAF
Aggregates many rows down to a single result. Unity Catalog can't govern these: they live only in the session, written in Python as pandas Series-to-scalar UDFs or, on dedicated access mode, in Scala.