Importing tables as datasets¶

The “import tables as datasets” feature is available through the API, both for Hive and SQL tables

Importing SQL tables¶

project = client.get_project("MYPROJECT")

import_definition = project.init_tables_import()
import_definition.add_sql_table("my_sql_connection", "schema_of_the_table", "name_of_the_table")

prepared_import = import_definition.prepare()
future = prepared_import.execute()

import_result = future.wait_for_result()

Importing Hive tables¶

project = client.get_project("MYPROJECT")

import_definition = project.init_tables_import()
import_definition.add_hive_table("hive_database", "hive_table_name")

prepared_import = import_definition.prepare()
future = prepared_import.execute()

import_result = future.wait_for_result()

Reference documentation¶

class dataikuapi.dss.project.TablesImportDefinition(client, project_key)¶

Temporary structure holding the list of tables to import

add_hive_table(hive_database, hive_table)¶: Add a Hive table to the list of tables to import

add_sql_table(connection, schema, table)¶: Add a SQL table to the list of table to import

prepare()¶

Run the first step of the import process. In this step, DSS will check the tables whose import you have requested and prepare dataset names and target connections

Returns:	a `TablesPreparedImport` object that allows you to finalize the import process
Return type:	`TablesPreparedImport`

class dataikuapi.dss.project.TablesPreparedImport(client, project_key, candidates)¶

Result of preparing a tables import. Import can now be finished

execute()¶

Starts executing the import in background and returns a dataikuapi.dss.future.DSSFuture to wait on the result

Returns:	a future to wait on the result
Return type:	`dataikuapi.dss.future.DSSFuture`