What Big Data Solution is capable of executing Spark-based workloads?

Oracle Data Flow (ODF) is the correct answer as it is specifically designed to support the execution of Spark-based workloads. ODF is a fully managed service that enables developers to run Apache Spark applications without worrying about the underlying infrastructure. This solution allows users to create, manage, and execute data processing tasks in a scalable manner by leveraging the capabilities of Apache Spark.

The architecture of Oracle Data Flow is optimized for processing large volumes of data using the Spark framework, providing built-in functionalities for data ingestion, processing, and storage, making it well-suited for applications that require high-performance data analytics.

In contrast, while Apache Hadoop supports a broader ecosystem that can accommodate Spark through its YARN resource manager, it is fundamentally designed around its own MapReduce programming model. Microsoft Azure Data Lake is primarily focused on providing storage and analytics capabilities on Azure but does not inherently execute Spark workloads by itself; rather, it can integrate with Azure Databricks to achieve that. Apache Flink, while capable of performing stream processing and batch processing and can work alongside Spark, is not exclusively a Spark-based workload executor, thus is not the best fit in this context.