Which pattern describes source data that is moved into a BigQuery table in a single operation?

The pattern that describes source data moved into a BigQuery table in a single operation is best described as a Batch Load. In this approach, a large volume of data is processed and transferred all at once, rather than in smaller increments or constant streams. This is particularly effective for scenarios where data is aggregated or processed periodically, allowing for efficient handling of large datasets.

Batch loading is advantageous in numerous situations, such as data migration tasks or scheduled data imports from various sources where the requirement is to update the BigQuery tables with a cumulative dataset at specific intervals rather than in real-time. This can help optimize performance and resource usage since BigQuery can efficiently execute and optimize the loading process for large data volumes all at once.

The other options, such as incremental, real-time, and stream loads, cater to different kinds of data movement and use cases. Incremental loads involve updating only the new or changed data rather than transferring everything, while real-time loads typically involve continuous movement of data, and stream loads allow for near-instantaneous updates to the tables with low latency as data arrives. However, these methods do not represent the characteristic of moving all data at once, which is why Batch Load is the correct choice.