What is a key advantage of using workflows in Databricks?

Using workflows in Databricks significantly streamlines the process of automating repetitive ETL (Extract, Transform, Load) tasks. By establishing a series of steps that can run in sequence or in parallel, workflows enable users to efficiently manage data ingestion, transformation, and loading into various storage systems or databases without the need for manual intervention each time. This automation not only saves time but also reduces human error, improving data consistency and reliability across the pipeline.

Workflows can often be scheduled to run at specific times or triggered by certain events, ensuring that data processing can be regularly maintained without needing constant supervision. This makes them an invaluable tool for data engineers and data scientists, who can focus more on analysis and model development rather than on repetitive coding and task execution.