Estimated time to complete: 30 minutes
There are many ways to run workloads on Databricks. For automated jobs, orchestration tools, or regularly scheduled tasks, we recommend using the Databricks API or jobs scheduler.
For development work, Databricks has built-in notebooks for rapid iteration. You learned about Databricks notebooks in Task 1 of this pathway. If you prefer an IDE or tool other than Databricks notebooks, the Databricks Connect library allows you to run code from a local machine on a remote Databricks cluster.
For more information on Databricks Connect, including setup, troubleshooting, and limitations, see Databricks Connect.
When you are done, return to this page and click this button to continue the Getting Started path for data engineers: