Best Of

@jaeW_at_Onyx I appreciate the response. We are unfortunately the consumers of the data and have nothing to do with that side of the data train! Our current solution has been to just hoover in ALL of the data with no constraints which is something like 27M records of historical data...takes like 30+ minutes to refresh. I think that we will soon be implementing a date range of nothing older than 90 days, so I think that will solve all of our problems!

Again, thank you.

@jaeW_at_Onyx Thanks a lot for your advice, I'm in an internal race competing against Airflow. But again, if I can prove Domo can do Jupyter, it will win the race, as with Airflow we need to support the VM, the linux and Airflow setup. With Domo we don't lose that many resources doing SysAdmin tasks.

@jaeW_at_Onyx wow, thanks, those videos of creating Jupyter notebooks inside Domo seem to be the solution. We were thinking about having a separate ETL process in Airflow and from there call Domo, but that will make us have to maintain both Domo and Airflow, and we prefer, if possible, just one.

So it will be:

use domo json connector to pull dataset from third party REST API ->

use snowflake writeback connector to push extracted data to our Datalake ->

use jupyter notebook in domo to connect to Snowflake and run the stored procedure ->

use normal Snowflake connector to pull resulting table created by the stored procedure into domo

Do we need to ask for the Domo Data Science package in order to be able to create a Jupyter notebook?