Final answer:
Data in a data warehouse differentiates from data in a database because it is focused on aggregating historical data for analysis and is not designed for transaction processing. Data warehouses store large volumes of historical data for trend analysis, unlike transactional databases which capture and manage real-time transaction data.
Step-by-step explanation:
Data in a data warehouse can be differentiated from data in a database in that the former: b. is focused on a specific area. This means that data warehouses are designed to provide a long-term, aggregated historical view of data, typically to be used for analysis and business intelligence. Data within a data warehouse is structured specifically for query and analysis rather than transaction processing. It contains large volumes of data from past periods which are generally categorized and summarized in a way that facilitates strategic decision making.
On the other hand, a traditional database is often used more for day-to-day operations, focusing on the speedy processing of transactions. This type of database is typically referred to as an Online Transaction Processing (OLTP) system, and it captures raw transaction data in real-time. Because it is designed to manage transactions, the emphasis is on the immediate availability, integrity, and consistency of the data.
A data warehouse, unlike a transactional database, does not focus on capturing and managing transactions. Instead, it is designed to integrate multiple data sources into a single, cohesive structure for historical reporting and analysis. Unlike traditional databases, data warehouses don't only keep recent activity in memory; they store vast amounts of historic data to allow for trend analysis over time.