What types of tasks does Databricks Workflows support?

Databricks Workflows is designed to facilitate the orchestration and execution of various tasks that are primarily focused on data processing and machine learning. It provides a streamlined way for users to define and schedule a series of interconnected jobs, including data ingestion from various sources, data transformation activities, and the training of machine learning models.

By supporting tasks for data ingestion and machine learning, Workflows enables organizations to automate their data pipelines, ensuring that data is prepared and ready for analysis or model training without manual intervention. This flexibility is crucial for data-driven projects, where maintaining an efficient and timely workflow can significantly impact decision-making and operational efficiency.

The other options do not align with the core functionalities of Databricks Workflows. While data warehousing might involve data management tasks, it does not encapsulate the full range of capabilities that Databricks targets. Human resource management and software development tasks fall outside the primary focus of the Databricks platform, which is centered around data engineering, data science, and analytics.