UC_COMMAND_NOT_SUPPORTED.WITHOUT_RECOMMENDATION in shared access mode

[Awoke101]

I'm using a shared access cluster and am getting this error while trying to upload to Qdrant. This is the error. Anyway I can make it worked on shared access mode? It works on the personal cluster.

[UC_COMMAND_NOT_SUPPORTED.WITHOUT_RECOMMENDATION] The command(s): AppendData are not supported in Unity Catalog.  SQLSTATE: 0AKUC
File <command-2604385794497207>, line 65
     48 #embeddings_df = embeddings_df.limit(5)
     50 options = {
     51     "qdrant_url": QDRANT_GRPC_URL,
     52     "api_key": QDRANT_API_KEY,
   (...)
     60     "batch_size":"128",
     61 }
     63 embeddings_df.write.format("io.qdrant.spark.Qdrant").options(**options).mode(
     64     "append"
---> 65 ).save()

File /databricks/spark/python/pyspark/sql/connect/readwriter.py:670, in DataFrameWriter.save(self, path, format, mode, partitionBy, **options)
    668     self.format(format)
    669 self._write.path = path
--> 670 self._spark.client.execute_command(
    671     self._write.command(self._spark.client), self._write.observations
    672 )
File /databricks/spark/python/pyspark/sql/connect/client/core.py:1203, in SparkConnectClient.execute_command(self, command, observations, extra_request_metadata)
   1201     req.user_context.user_id = self._user_id
   1202 req.plan.command.CopyFrom(command)
-> 1203 data, _, _, _, properties = self._execute_and_fetch(
   1204     req, observations or {}, extra_request_metadata
   1205 )
   1206 if data is not None:
   1207     return (data.to_pandas(), properties)
File /databricks/spark/python/pyspark/sql/connect/client/core.py:1624, in SparkConnectClient._execute_and_fetch(self, req, observations, extra_request_metadata, self_destruct)
   1621 schema: Optional[StructType] = None
   1622 properties: Dict[str, Any] = {}
-> 1624 for response in self._execute_and_fetch_as_iterator(
   1625     req, observations, extra_request_metadata or []
   1626 ):
   1627     if isinstance(response, StructType):
   1628         schema = response
File /databricks/spark/python/pyspark/sql/connect/client/core.py:1601, in SparkConnectClient._execute_and_fetch_as_iterator(self, req, observations, extra_request_metadata)
   1599                     yield from handle_response(b)
   1600 except Exception as error:
-> 1601     self._handle_error(error)
File /databricks/spark/python/pyspark/sql/connect/client/core.py:1910, in SparkConnectClient._handle_error(self, error)
   1908 self.thread_local.inside_error_handling = True
   1909 if isinstance(error, grpc.RpcError):
-> 1910     self._handle_rpc_error(error)
   1911 elif isinstance(error, ValueError):
   1912     if "Cannot invoke RPC" in str(error) and "closed" in str(error):
File /databricks/spark/python/pyspark/sql/connect/client/core.py:1985, in SparkConnectClient._handle_rpc_error(self, rpc_error)
   1982             info = error_details_pb2.ErrorInfo()
   1983             d.Unpack(info)
-> 1985             raise convert_exception(
   1986                 info,
   1987                 status.message,
   1988                 self._fetch_enriched_error(info),
   1989                 self._display_server_stack_trace(),
   1990             ) from None
   1992     raise SparkConnectGrpcException(status.message) from None

 This is the code.

#embeddings_df = embeddings_df.limit(5)

options = {
    "qdrant_url": QDRANT_GRPC_URL,
    "api_key": QDRANT_API_KEY,
    "collection_name": QDRANT_COLLECTION_NAME,
    "vector_fields": "dense_vector",
    "vector_names": "dense",
    "schema": embeddings_df.schema.json(),
    "batch_size":"128",
}

embeddings_df.write.format("io.qdrant.spark.Qdrant").options(**options).mode(
    "append"
).save()