Which of the following workloads are best suited for Autonomous Data Warehouse?

An Autonomous Data Warehouse is designed to simplify the process of data management and analysis by automating many of the traditionally manual tasks associated with data warehousing. This capability makes it well-suited for a variety of workloads.

Data marts are subsets of data warehouses that are focused on specific business lines or teams. An Autonomous Data Warehouse can easily accommodate these targeted datasets, allowing for efficient querying and analytics that provide relevant insights for those specific areas.

For analytics, the Autonomous Data Warehouse streamlines the process of data retrieval and processing, enabling users to perform complex queries and generate insights rapidly. It benefits organizations by allowing analysts to derive business intelligence without extensive admin and setup overhead.

A data lake, although typically used for storing large volumes of unstructured or semi-structured data, can also benefit from an Autonomous Data Warehouse setup. It allows organizations to integrate their data lakes with structured data processing capabilities, enabling users to analyze data stored in different formats within one environment.

Thus, since an Autonomous Data Warehouse can effectively support data marts, analytics processes, and the integration of data lakes, it is accurate to conclude that it is suitable for all of these workloads.