When working with Azure Synapse Analytics, which factor primarily affects the performance of queries to analytical stores?

The performance of queries to analytical stores within Azure Synapse Analytics is primarily influenced by the size and complexity of the data. When dealing with large datasets or intricate queries that involve multiple joins, aggregations, or subqueries, the system requires more computational resources and time to process these requests.

As the volume of data grows, the engine needs to perform more operations, which can lead to increased latency and longer execution times. Similarly, complex data structures may result in more challenging query plans that can further impact performance, especially if they require extensive resource allocation for optimal execution.

While other factors such as the number of active users, processing pool configuration, and geographical location also play roles in overall performance, the intrinsic properties of the data being queried—its size and complexity—directly dictate how effectively the queries can be processed by the Azure Synapse Analytics environment. Thus, focusing on optimizing these aspects can yield significant improvements in query execution times and overall system performance.