In their Magic Quadrant for Data Warehouse Database Management Systems, 2006 study, Gartner Group
identified several data warehousing workloads that typified the various use cases found at customer sites
who were actively involved in business intelligence. These workloads were:
• Continuous (near-real-time) data loading — similar to an online transaction processing (OLTP)
workload
• Large numbers of standard reports ranging in the thousands per day, requiring Structured Query
Language (SQL) tuning and index creation
• An increasing number of true ad hoc query users with a random, unpredictable use of the data
• Analytics and BI-oriented functionality in OLTP applications
Translating these workloads into practical data warehousing installations yields the following use cases:
1. Small, semi real-time data marts
2. Continuous, real-time/query data warehousing
3. Traditional, standard reporting warehousing
4. Massive historical, with ad-hoc queries warehousing
5. BI, analytic in OLTP applications (an emerging trend)
Let’s next examine each of these use cases in detail so the various critical distinctions can be understood.
No comments:
Post a Comment