Google BigQuery offers several methods for retrieving row counts from tables and views, each with its own set of advantages and challenges. The `COUNT(*)` function provides an accurate count but can be resource-intensive for large tables. Using the `INFORMATION_SCHEMA` is efficient and cost-effective but offers only approximate counts that may not reflect recent changes. The BigQuery API allows for programmatic access and consistency in results, though it requires proper authentication and could lead to increased network overhead. Table statistics provide a fast estimate of row counts without full table scans, but these may not be up-to-date after recent modifications. Each method has trade-offs, such as the balance between accuracy, resource consumption, and cost, and the most suitable approach depends on specific needs, such as table size and frequency of updates.