Relational databases have traditionally been the data for Line of Business (LOB) applications. These applications require a database that supports thousands of concurrent users. Figure “write-optimized” 1-2 shows the characteristics of a LOB application. Line of Business application characteristics Figure1-2: 1 lists some characteristics of an LOB workload, which is also often referred to as On Line Transaction Processing or OLTP. Characteristic Line of Business (OLTP) Concurrent Many (100s - 10,000+) Database Access Stored Procedures (Stored Procs.), SQL Transaction Scope One record several records – Database Operations Singleton Selects, Inserts, Updates, Deletes Data Volatility Volatile, Current Data Table 1-1: LOB workload characteristics Everything works well until the business starts running large reports against the database, as shown in Figure 1-3. Figure 1-3: Reporting against a LOB database In such scenarios, you’ll start seeing LOB database reporting issues, including: Blocking and locking - Reports of any size will request many rows from many tables joined together in one Select statement. This results in the report blocking LOB application activity. Poor performance Report performance is often sub-optimal even if there’s no blocking simply – because of the amount of joins required to produce the results. Lack of history Many reports, such as trend analysis, require historical data to report trends – over. LOB systems overwrite historical data with current data, making it impossible to report on historical data. Scope Organizations frequently need to report on enterprise level information. This requires – information from multiple subject areas, which can span multiple LOB systems. These LOB reporting limitations then become requirements for a data warehouse: Scope The ability to report on the picture” – “big History The ability to report on historical information – Read optimized Data models that are tuned in support of large queries – requirements in turn feed into the following challenges for properly designing and implementing the data warehouse:
Relational databases have traditionally been the data for Line of Business (LOB) applications. These applications require a “write-optimized” database that supports thousands of concurrent users. Figure 1-2 shows the characteristics of a LOB application. Figure1-2: Line of Business application characteristics 1 lists some characteristics of an LOB workload, which is also often referred to as On Line Transaction Processing or OLTP. Characteristic Line of Business (OLTP) Concurrent Many (100s - 10,000+) Database Access Stored Procedures (Stored Procs.), SQL Transaction Scope One record – several records Database Operations Singleton Selects, Inserts, Updates, Deletes Data Volatility Volatile, Current Data Table 1-1: LOB workload characteristics Everything works well until the business starts running large reports against the database, as shown in Figure 1-3. Figure 1-3: Reporting against a LOB database In such scenarios, you’ll start seeing LOB database reporting issues, including: Blocking and locking - Reports of any size will request many rows from many tables joined together in one Select statement. This results in the report blocking LOB application activity. Poor performance – Report performance is often sub-optimal even if there’s no blocking simply because of the amount of joins required to produce the results. Lack of history – Many reports, such as trend analysis, require historical data to report trends over. LOB systems overwrite historical data with current data, making it impossible to report on historical data. Scope – Organizations frequently need to report on enterprise level information. This requires information from multiple subject areas, which can span multiple LOB systems. These LOB reporting limitations then become requirements for a data warehouse: Scope – The ability to report on the “big picture” History – The ability to report on historical information Read optimized – Data models that are tuned in support of large queries requirements in turn feed into the following challenges for properly designing and implementing the data warehouse: