What is the purpose of incremental refresh in a large fact-table model?

What is the purpose of incremental refresh in a large fact-table model?

• A. To allow real-time data ingestion
• B. To reduce refresh load times
• C. To improve DAX performance
• D. To store historical data

Answer: (B)

Incremental refresh updates only the new or changed data instead of reloading the entire large fact table. For big datasets, a full refresh can take a long time because every row must be read and processed. By partitioning data (usually by a date) and refreshing just the recent partitions while leaving older ones intact, refresh times and resource usage drop dramatically. That’s why this approach is used—to make refreshing large datasets practical and faster—rather than enabling real-time data ingestion, improving DAX performance, or simply storing more history.

Incremental refresh updates only the new or changed data instead of reloading the entire large fact table. For big datasets, a full refresh can take a long time because every row must be read and processed. By partitioning data (usually by a date) and refreshing just the recent partitions while leaving older ones intact, refresh times and resource usage drop dramatically. That’s why this approach is used—to make refreshing large datasets practical and faster—rather than enabling real-time data ingestion, improving DAX performance, or simply storing more history.