Which system allows Databricks to provide a unified analytics platform?

The integration of big data processing and machine learning is fundamental to Databricks' unified analytics platform. This integration enables users to leverage large datasets for advanced analytics and machine learning in a seamless environment. Databricks combines various components of data processing, such as data ingestion, ETL (Extract, Transform, Load), and machine learning model training and deployment into a single platform. This eliminates the need for users to switch between different tools and environments, allowing them to perform comprehensive data science tasks from data preparation to model deployment in one cohesive ecosystem.

The other options do not encapsulate the essence of a unified analytics platform. A highly restricted user control system may emphasize security but does not contribute to the integration of capabilities that define a unified platform. A separate cloud service for storage management would indicate a division in functionality rather than a unified approach, and focusing only on batch processing limits the flexibility and responsiveness that real-time analytics and machine learning demands. Thus, the successful combination of big data processing and machine learning truly underpins Databricks as a unified analytics solution.