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import logging logging.getLogger().setLevel(logging.WARN) log = logging.getLogger("DATABRICKS-LOGGER") log.warning("Hello")

I don't think that level of customisation is provided. However, I can suggest some workarounds:REST API: Create a job on the fly with desired name within ADF and trigger it using REST API in Web activity. This way you can track job completion status ...

Awesome answer, I will try the first approach. I think it is a less intrusive solution than changing the rules of my pipeline in development scenarios. This way, I can maintain a general pipeline for deployment across all environments. We plan to imp...

@daniel_sahal , this is the code snippet:lsn_incr_batch = spark.sql(f"""select start_lsn,tran_begin_time,tran_end_time,tran_id,tran_begin_lsn,cast('{current_run_ts}' as timestamp) as appendedfrom externaldb.cdc.lsn_time_mappingwhere tran_end_time > '...

@amar1995 - Can you try this streaming approach and see if it works for your use case (using autoloader) - https://kb.databricks.com/streaming/stream-xml-auto-loader

you can use databricks SDKhttps://docs.databricks.com/en/dev-tools/sdk-python.html#create-a-job 

Could you please share the entire error message?Are you running the code locally or on databricks?

I would like to share the following links https://www.databricks.com/product/machine-learning/large-language-models https://docs.databricks.com/en/large-language-models/index.html

Thanks everyone, I decided to use the Sparkcontext instead. dbutils.notebook.entry_point.getDbutils().notebook().getContext().toJson()