What is the batch size limit for dataflows?

The specified batch size limit for dataflows being 100-250k records per batch means that when handling data integration tasks, we can process a reasonable number of records in each operation, balancing efficiency with performance. This range is particularly important because it takes into account the constraints of processing power, memory usage, and the need to maintain a responsive system without overwhelming it with excessive data at once.

Using a batch size within this designated range leads to optimal performance when loading and transforming data within dataflows. By processing records in manageable groups, it minimizes the risk of errors and allows for easier debugging if issues arise. This structured approach is critical in analytics and business intelligence, ensuring that large datasets are handled effectively without risking system overload or significantly increasing processing time.

The other potential ranges either suggest batch sizes that are too small or exceed optimal thresholds, which may lead to inefficiencies, increased processing time, and challenges in managing data consistency and integrity.