14-06-2012, 04:26 PM
Data warehouse
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INTRODUCTION
• Along with the prevalence of the computer application technology, the foundation of the power management information system advances rapidly. The basic application systems have been founded and are running steadily, such as the power management information system (MIS), Automatic Meter Reading system, the Distribution Management System, the Automatic Mapping Management information System, the Substation Automation and the office automation (OA) system. It is an important resort to build higher level application making for the analysis and the decision-making using the preceding computer technologies and the basic application systems. It can help the electric power corporations enhance the management level and the circulation efficiency, improve service quality and finally upgrade the enterprise competition. The method to solve the problem is building the data warehouse.
What is a Data Warehouse?
A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.
In addition to a relational database, a data warehouse environment includes an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users.
Hybrid design
• Data warehouse (DW) solutions often resemble hub and spoke architecture. Legacy systems feeding the DW/BI solution often include customer relationship management (CRM) and enterprise resource planning solutions (ERP), generating large amounts of data. To consolidate these various data models, and facilitate the extract transform load (ETL) process, DW solutions often make use of an operational data store (ODS). The information from the ODS is then parsed into the actual DW. To reduce data redundancy, larger systems will often store the data in a normalized way. Data marts for specific reports can then be built on top of the DW solution.
Advantages
Functionality of Data Warehouses:
Data warehouses exist to facilitate complex, data-intensive and frequent ado queries. Data warehouses must provide far greater and more efficient query support than is demanded of transactional databases. The data warehouse access component supports enhanced spreadsheet functionality, efficient query processing, structured queries, and adhoc queries, data mining and materialized views. Particularly enhanced spreadsheet functionality includes support for state-of-the art spreadsheet applications as well as for OLAP applications programs. These provide preprogrammed functionalities such as the following:
Disadvantages
However, there are considerable disadvantages involved in moving data from multiple, often highly disparate, data sources to one data warehouse that translate into long implementation time, high cost, lack of flexibility, dated information and limited capabilities:
• Major data schema transforms from each of the data sources to one schema in the data warehouse, which can represent more than 50% of the total data warehouse effort
• Data owners lose control over their data, raising ownership (responsibility and accountability), security and privacy issues
• Long initial implementation time and associated high cost
• Adding new data sources takes time and associated high cost
Conclusion
However, data warehouses are still an expensive solution and typically found in large firms. The development of a central warehouse is a huge undertaking and capital intensive with large, potentially unmanageable risks