Nonvolatile Data
Data extracted from the various operational systems and pertinent data obtained from outside sources are transformed, integrated, and stored in the data warehouse, the data in the data warehouse is not intended to run the day-to-day business. When you want to process the next dater received from a customer, you do not look into the data ware-house to find the current stock status. The operational order entry application is meant for that purpose. In the data warehouse, you keep the extracted stock status data as snap-shots over time. You do not update the data warehouse every time you process a single order.
Data from the operational systems are moved into the data warehouse at specific intervals. Depending on the requirements or the business, these data movements take place twice a day, once a day, once a week, or once in two weeks. In fact, in a typical data warehouse, data movements to different data sets may take place at different frequencies. The .changes to the attributes of the products may he moved once a week. Any revisions to geographical setup may be moved once a month. The units of sales may be moved once a day. You plan and schedule the data movements or data loads based on the requirements or your users.
As illustrated in Figure 2-3, every business transaction does not update the data ilk the data warehouse. The business transactions update the operational system databases in real time. We add, change. or delete data from an operational system as each transaction hap-pens but do not usually update: the data in the data warehouse. You not delete the data in the data warehouse in real lime. Once the data is captured in the data warehouse, you do not run individual transactions to change the data there. Data updates are commonplace in an operational database; not Si) in it data warehouse. The data in a warehouse is not as the data in an operational database is. The data in it data warehouse is primarily for query and analysis.
Data extracted from the various operational systems and pertinent data obtained from outside sources are transformed, integrated, and stored in the data warehouse, the data in the data warehouse is not intended to run the day-to-day business. When you want to process the next dater received from a customer, you do not look into the data ware-house to find the current stock status. The operational order entry application is meant for that purpose. In the data warehouse, you keep the extracted stock status data as snap-shots over time. You do not update the data warehouse every time you process a single order.
Data from the operational systems are moved into the data warehouse at specific intervals. Depending on the requirements or the business, these data movements take place twice a day, once a day, once a week, or once in two weeks. In fact, in a typical data warehouse, data movements to different data sets may take place at different frequencies. The .changes to the attributes of the products may he moved once a week. Any revisions to geographical setup may be moved once a month. The units of sales may be moved once a day. You plan and schedule the data movements or data loads based on the requirements or your users.
As illustrated in Figure 2-3, every business transaction does not update the data ilk the data warehouse. The business transactions update the operational system databases in real time. We add, change. or delete data from an operational system as each transaction hap-pens but do not usually update: the data in the data warehouse. You not delete the data in the data warehouse in real lime. Once the data is captured in the data warehouse, you do not run individual transactions to change the data there. Data updates are commonplace in an operational database; not Si) in it data warehouse. The data in a warehouse is not as the data in an operational database is. The data in it data warehouse is primarily for query and analysis.
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