What technique can optimize query performance in BigQuery when tables are not partitioned or clustered?

Batching your updates and inserts can optimize query performance in BigQuery, particularly when dealing with large datasets that aren't partitioned or clustered. When operations are batched, they help reduce the number of write operations, which can otherwise incur additional overhead and latency. This approach minimizes the impact of those operations on performance, enabling more efficient resource usage, as BigQuery processes the data in larger chunks rather than single-row transactions.

In environments where tables lack partitioning or clustering, optimizations become more critical because these techniques typically enhance performance by organizing the data. Without them, focusing on how data is handled during updates and inserts is vital for maintaining efficiency.

For additional context, while using the LIMIT clause can help reduce the volume of data processed during queries, it does not specifically address the impact of data writes or updates on performance. Filtering data late can result in handling unnecessary data, and performing self-joins can often lead to increased complexity and processing time, potentially degrading performance rather than optimizing it.