![]() You can append data to an existing table using the following syntax: Python (employees_table.write The default behavior attempts to create a new table and throws an error if a table with that name already exists. See the following example: Python (employees_table.write ![]() Saving data to tables with JDBC uses similar configurations to reading. Scala display(employees_lect("age", "salary").groupBy("age").avg("salary")) ![]() You can run queries against this JDBC table: Python display(employees_lect("age", "salary").groupBy("age").avg("salary")) Spark automatically reads the schema from the database table and maps its types back to Spark SQL types. SQL CREATE TEMPORARY VIEW employees_table_vw Note that each database uses a different format for the. You must configure a number of settings to read data using JDBC. To reference Databricks secrets with SQL, you must configure a Spark configuration property during cluster initilization.įor a full example of secret management, see Secret workflow example. Val password = (scope = "jdbc", key = "password") Scala val username = (scope = "jdbc", key = "username") Password = (scope = "jdbc", key = "password") For example: Python username = (scope = "jdbc", key = "username") Databricks recommends using secrets to store your database credentials. The examples in this article do not include usernames and passwords in JDBC URLs.
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