createOrReplaceGlobalTempView ( 'my_data' ) dbutils. You can only return one string using (), but since called notebooks reside in the same JVM, you can // return a name referencing data stored in a temporary view. Example 1 - returning data through temporary views. run ( 'LOCATION_OF_CALLEE_NOTEBOOK', 60 ) print ( json. dumps ()) # In caller notebook result = dbutils. # In callee notebook import json dbutils. # To return multiple values, you can use standard JSON libraries to serialize and deserialize results. load ( returned_table )) # Example 3 - returning JSON data. run ( 'LOCATION_OF_CALLEE_NOTEBOOK', 60 ) display ( spark.

exit ( 'dbfs:/tmp/results/my_data' ) # In caller notebook returned_table = dbutils. load ( 'dbfs:/tmp/results/my_data' ) dbutils. rm ( '/tmp/results/my_data', recurse = True ) spark. # For larger datasets, you can write the results to DBFS and then return the DBFS path of the stored data. get ( '' ) display ( table ( global_temp_db + '.' + returned_table )) # Example 2 - returning data through DBFS. run ( 'LOCATION_OF_CALLEE_NOTEBOOK', 60 ) global_temp_db = spark. exit ( 'my_data' ) # In caller notebook returned_table = dbutils. # You can only return one string using (), but since called notebooks reside in the same JVM, you can # return a name referencing data stored in a temporary view. # Example 1 - returning data through temporary views. However, you can use () to invoke an R notebook. These methods, like all of the dbutils APIs, are available only in Python and Scala. Unlike %run, the () method starts a new job to run the notebook. You can also create if-then-else workflows based on return values or call other notebooks using relative paths. For example, you can get a list of files in a directory and pass the names to another notebook, which is not possible with %run. This allows you to build complex workflows and pipelines with dependencies. The dbutils.notebook API is a complement to %run because it lets you pass parameters to and return values from a notebook. When you use %run, the called notebook is immediately executed and the functions and variables defined in it become available in the calling notebook. You can also use it to concatenate notebooks that implement the steps in an analysis. You can use %run to modularize your code, for example by putting supporting functions in a separate notebook. The %run command allows you to include another notebook within a notebook. Use the Databricks notebook and file editorĬomparison of %run and ().Open or run a Delta Live Tables pipeline from a notebook.

Target notebooks