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Partitioning in BigQuery is significant primarily because it improves query performance and cost management. When data is partitioned, it allows large datasets to be divided into smaller, more manageable segments based on specific criteria, such as timestamps or other relevant fields. This division enables BigQuery to process only the relevant partitions of data that are necessary for a given query, significantly reducing the amount of data scanned.

This efficiency in data scanning directly contributes to improved query performance, as fewer data records need to be processed to return results. Additionally, because BigQuery charges for data processed during queries, partitioning helps manage costs effectively—users can save on query expenses by minimizing the volume of data scanned.

While partitioning can relate to other concepts such as indexing, encryption, and redundancy, these are not its primary benefits. Partitioning specifically serves as a mechanism to optimize performance and manage costs, making it a critical practice for effective data management in BigQuery.