Create a TabularDataset to represent tabular data in SQL databases. ``from_sql_query``` creates a Tabular Dataset object , which defines the operations to load data from SQL databases into tabular representation. For the data to be accessible by Azure Machine Learning, the SQL database specified by query must be located in a Datastore and the datastore type must be of a SQL kind. Column data types are read from data types in SQL query result. Providing `set_column_types` will override the data type for the specified columns in the returned Tabular Dataset.

  validate = TRUE,
  set_column_types = NULL,
  query_timeout = 30L



A SQL-kind datastore and a query


Boolean to validate if data can be loaded from the returned dataset. Defaults to True. Validation requires that the data source is accessible from the current compute.


A named list to set column data type, where key is column name and value is data type.


Sets the wait time (as an int, in seconds) before terminating the attempt to execute a command and generating an error. The default is 30 seconds.


A TabularDataset object


# create tabular dataset from a SQL database in datastore
datastore <- get_datastore(ws, 'sql-db')
query <- data_path(datastore, 'SELECT * FROM my_table')
tab_ds <- create_tabular_dataset_from_sql_query(query, query_timeout = 10)

# use `set_column_types` param to set column data types
data_types <- list(ID = data_type_string(),
                   Date = data_type_datetime('%d/%m/%Y %I:%M:%S %p'),
                   Count = data_type_long(),
                   Latitude = data_type_double(),
                   Found = data_type_bool())

set_tab_ds <- create_tabular_dataset_from_sql_query(query, set_column_types = data_types)

See also

data_path() data_type_datetime() data_type_bool() data_type_double() data_type_string() data_type_long()