Partitioning a lakehouse table by CustomerID primarily enables which performance benefit?

Partitioning a lakehouse table by CustomerID primarily enables which performance benefit?

• A. Faster write operations
• B. Faster prune and query performance on CustomerID
• C. Automatic data validation
• D. Reduced storage cost

Answer: (B)

Partitioning a lakehouse table by CustomerID primarily enables partition pruning, which speeds up queries that filter on that column. By organizing data into separate partitions for each CustomerID, the query engine can skip entire partitions that don’t match the filter, so it reads far fewer files. This reduces I/O and scan time, leading to faster query performance when you’re filtering by CustomerID. It isn’t about faster writes—partitioning doesn’t inherently make writes faster and can even add overhead if there are many partitions. It isn’t automatic data validation, and it doesn’t inherently reduce storage cost, since the data itself isn’t compressed or eliminated by partitioning.

Partitioning a lakehouse table by CustomerID primarily enables partition pruning, which speeds up queries that filter on that column. By organizing data into separate partitions for each CustomerID, the query engine can skip entire partitions that don’t match the filter, so it reads far fewer files. This reduces I/O and scan time, leading to faster query performance when you’re filtering by CustomerID.

It isn’t about faster writes—partitioning doesn’t inherently make writes faster and can even add overhead if there are many partitions. It isn’t automatic data validation, and it doesn’t inherently reduce storage cost, since the data itself isn’t compressed or eliminated by partitioning.