The correct answer is Data is physically laid out as one record after another in row oriented storage and as groups of values per attribute in column oriented storage.

This is correct because in a row oriented storage layout each complete record is written sequentially so reading or writing whole rows is efficient for transaction style workloads. In contrast a column oriented storage layout stores each attribute together so scanning a few columns across many rows is much faster and compression is often better.

The two approaches have trade offs and are chosen based on workload. A row oriented storage design favors fast retrieval of full rows and simple inserts. A column oriented storage design favors analytics that read many rows but only a subset of columns and it can reduce I O through better compression.

BigQuery stores data using a column oriented format is not the best choice for this question because it states a product fact instead of describing the general difference between row and column organization. The question asks about the comparison of storage layouts and not about a single service.

Row oriented systems generally allow faster row inserts while column oriented systems speed up queries that read few columns is too broad to be marked correct here. Modern systems include hybrid and optimized ingestion paths so insert and query performance depends on implementation and workload and is not universally captured by that single generalization.

Row based storage is always more costly than column based storage is wrong because the word always makes it an absolute claim. Cost and efficiency depend on data types, compression, access patterns, and the specific database implementation.

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