An Azure Cosmos DB for NoSQL account contains a container (Contained) with the analytical store enabled. You need to process Contained’s data in near‑real‑time and write results to a data warehouse in an Azure Synapse Analytics workspace using a runtime engine in the workspace, minimizing data movement. Which Synapse pool should you use?

Apache Spark pools are ideal for near real-time data processing and analytics on operational data, especially when integrated with Azure Cosmos DB's analytical store. This integration allows Spark to directly query the analytical store without impacting the transactional store, minimizing data movement and latency. Spark's robust processing capabilities are well-suited for transforming and preparing data before writing it to a data warehouse. Serverless SQL pools are good for ad-hoc queries on data lakes but are not designed for continuous, near real-time processing of operational data from Cosmos DB. Dedicated SQL pools are optimized for large-scale data warehousing and batch analytics, not for near real-time processing of operational data. Data Explorer pools are used for time series and log analytics, which is not the primary use case here.