How does data latency in a data warehouse typically compare to application data?

How does data latency in a data warehouse typically compare to application data?

• A. Data warehouse data has lower latency
• B. Application data has less historical context
• C. Data warehouse data exhibits higher latency
• D. Both have identical latency

Answer: (C)

Data latency refers to the delay between when data is created or collected and when it becomes available for analysis or use. In a data warehouse environment, data is typically collected from various sources over time, processed, and then made available for querying and reporting. This process often involves several steps, such as extraction, transformation, and loading (ETL), which inherently introduces delays. As a result, data warehouse data often exhibits higher latency compared to application data, which is usually processed and accessed in real-time or near real-time. Applications often require immediate access to up-to-date information for operational purposes, leading to lower latency. Therefore, it is accurate to say that data warehouse data exhibits higher latency due to the nature of its collection and processing methods aimed at providing analytical insights from a broader historical dataset. In contrast, while application data may contain less historical context, it is not directly related to latency. Identical latency is also not reflective of the typical characteristics seen in data warehousing versus application data, as they serve different purposes and operate under varying data handling processes. Thus, option C correctly identifies the characteristic of data latency within the context of data warehouses.

Data latency refers to the delay between when data is created or collected and when it becomes available for analysis or use. In a data warehouse environment, data is typically collected from various sources over time, processed, and then made available for querying and reporting. This process often involves several steps, such as extraction, transformation, and loading (ETL), which inherently introduces delays.

As a result, data warehouse data often exhibits higher latency compared to application data, which is usually processed and accessed in real-time or near real-time. Applications often require immediate access to up-to-date information for operational purposes, leading to lower latency. Therefore, it is accurate to say that data warehouse data exhibits higher latency due to the nature of its collection and processing methods aimed at providing analytical insights from a broader historical dataset.

In contrast, while application data may contain less historical context, it is not directly related to latency. Identical latency is also not reflective of the typical characteristics seen in data warehousing versus application data, as they serve different purposes and operate under varying data handling processes. Thus, option C correctly identifies the characteristic of data latency within the context of data warehouses.