Which data aspect is typically characterized by higher latency in a data warehouse?

Which data aspect is typically characterized by higher latency in a data warehouse?

• A. Current data values
• B. Historical data availability
• C. Real-time data access
• D. Aggregated data structures

Answer: (B)

Higher latency in a data warehouse is typically associated with historical data availability. This is because data warehouses are designed to store and manage large volumes of historical data, which may not be updated as frequently as current data. The process of loading historical data into a data warehouse, which often involves extraction, transformation, and loading (ETL) processes, can introduce delays. This latency occurs as the warehouse consolidates and integrates data from various sources, ensuring the accuracy and integrity of the historical data before it becomes available for analysis. This characteristic of historical data provides organizations with valuable insights over time, enabling them to perform trend analysis and reporting. However, the trade-off for this extensive historical analysis is that the data may not represent the most current state of operations, hence the higher latency compared to the real-time or current data scenarios. In contrast, current data values, real-time data access, and aggregated data structures are typically designed for speed and efficiency, resulting in lower latency in terms of retrieval and query response times.

Higher latency in a data warehouse is typically associated with historical data availability. This is because data warehouses are designed to store and manage large volumes of historical data, which may not be updated as frequently as current data. The process of loading historical data into a data warehouse, which often involves extraction, transformation, and loading (ETL) processes, can introduce delays. This latency occurs as the warehouse consolidates and integrates data from various sources, ensuring the accuracy and integrity of the historical data before it becomes available for analysis.

This characteristic of historical data provides organizations with valuable insights over time, enabling them to perform trend analysis and reporting. However, the trade-off for this extensive historical analysis is that the data may not represent the most current state of operations, hence the higher latency compared to the real-time or current data scenarios.

In contrast, current data values, real-time data access, and aggregated data structures are typically designed for speed and efficiency, resulting in lower latency in terms of retrieval and query response times.