Prepare a high-level summary of the main requirements to evaluate DBMS products for data warehousing. Suppose you are selling the data warehouse idea to your users. How would you explain to them what multidimensional data analysis is and its advantages?
The Data Warehousing project group has invited you to provide an OLAP overview before making a commitment. The group's members are particularly concerned about the OLAP client/server architecture requirements and how OLAP will fit in the existing environment. Your job is to explain to them the main OLAP client/server components and architectures.© BrainMass Inc. brainmass.com October 25, 2018, 6:02 am ad1c9bdddf
The main requirements to evaluate DBMS products for data warehousing:
- In computing, a data warehouse (DW) is a database used for reporting and analysis. The data stored in the warehouse is uploaded from the operational systems. The data may pass through an operational data store for additional operations before it is used in the DW for reporting .
The main requirements to evaluate DBMS products i.e. DBMS evaluation criteria  for data warehousing are summarized below:
- Data management: DBMS should have the capability to support the management of large quantities of data, demonstrating experience in responding to real user demands while maintaining high availability .
- Data administration: It should have the feature, which facilitates to understand, predict and optimize resource usage, or provide tools and information for the database administrator to manually undertake the risk. It should also possess the optimizer facility .
- Scalability and suitability: It should provide necessary flexibility of hardware platform choice, in order to provide sufficient growth potential .
- Concurrent query management: The DBMS should have enough capability to support the multiple users performing a mixture of both simple and complex requests. It should provide facility like workload partitioning and balancing. It should have a concurrence model that can support data loads to the database .
- Proven data warehousing track record: Given that real-world implementation and proof is very important, the DBMS vendor should have the capability to offer reliable references, preferable from the same industry and with a similar data warehousing profile .
- Query performance: It should have a well-proven query performance by showing SQL optimizer strength, ability to join processing algorithms, techniques for handling specialized schema types, indexing methods and data portioning techniques.
Multidimensional data analysis and its advantages:
What is multidimensional analysis?
Multidimensional analysis takes data and turns it into highly explorable structures sometimes called cubes. These structures provide a multidimensional view of the data — for example, what product sold best in a specific region, during a particular time period for a specific sales channel. This view helps to provide a greater insight into the business and helps the concerned body to make more informed decisions .
Providing quick answers to commonly asked business questions is the core value of multidimensional analysis. Because it is designed around key business factors, the quality of answers obtained from this type of analysis is very high .
Multidimensional analysis uses dimensions and measures for analyzing given data. Dimensions are hierarchies and have one or more levels. The dimensions that is ...
Use the attached file.
- What is the scope of what can be considered a data warehousing failure?
- What generalizations apply across the cases?
- What do you find most interesting in the failure stories?
- Do they provide any insights about how a failure might be avoided?View Full Posting Details