Databases and data warehouses clearly make it easier for people to access all ki
ID: 3758348 • Letter: D
Question
Databases and data warehouses clearly make it easier for people to access all kinds of information. This will lead to great debates in the area of privacy. Should organizations be left to police themselves with respect to providing access to information or should the go vemment impose privacy legislation? Answer this question with respect to customer information shared by organizations. employee information shared within a specific organization; and business information available to customers. Some people used to believe that data warehouses would quickly replace databases for both online transaction processing (OLTP) and online analytical processing (OLAP). Of course, they were wrong Why can data warehouses not replace databases and become "operational data warehouse"? How radically would data warehouses (and their data-mining tools) have to change to become a viable replacement for databases? Would they then essentially become databases that simply supported OLAP? Why or why not?Explanation / Answer
Dataware Housing:
Data warehousing is the process of constructing and using a data warehouse. A data warehouse is constructed by integrating data from multiple heterogeneous sources that support analytical reporting, structured and/or ad hoc queries, and decision making. Data warehousing involves data cleaning, data integration, and data consolidations.
In this Dataware housing some modifications and changings are avilable in forthor companys it will be provide some security in this procees no one can acess this modification process.
Tuning Production Strategies - The product strategies can be well tuned by repositioning the products and managing the product portfolios by comparing the sales quarterly or yearly.
Customer Analysis - Customer analysis is done by analyzing the customer's buying preferences, buying time, budget cycles, etc.
Operations Analysis - Data warehousing also helps in customer relationship management, and making environmental corrections. The information also allows us to analyze business operations.
Query-driven approach needs complex integration and filtering processes.
This approach is very inefficient.
It is very expensive for frequent queries.
This approach is also very expensive for queries that require aggregations.
Contrasting OLTP and Data Warehousing Environments
Data warehouses and OLTP systems have very different requirements. Here are some examples of differences between typical data warehouses and OLTP systems.
Data warehouses are designed to accommodate ad hoc queries. You might not know the workload of your data warehouse in advance, so a data warehouse should be optimized to perform well for a wide variety of possible query operations.
OLTP systems support only predefined operations. Your applications might be specifically tuned or designed to support only these operations.
A data warehouse is updated on a regular basis by the ETL process (run nightly or weekly) using bulk data modification techniques. The end users of a data warehouse do not directly update the data warehouse.
In OLTP systems, end users routinely issue individual data modification statements to the database. The OLTP database is always up to date, and reflects the current state of each business transaction.
Data warehouses often use denormalized or partially denormalized schemas (such as a star schema) to optimize query performance.
OLTP systems often use fully normalized schemas to optimize update/insert/delete performance, and to guarantee data consistency.
A typical data warehouse query scans thousands or millions of rows. For example, "Find the total sales for all customers last month."
A typical OLTP operation accesses only a handful of records. For example, "Retrieve the current order for this customer."
Data warehouses usually store many months or years of data. This is to support historical analysis.
OLTP systems usually store data from only a few weeks or months. The OLTP system stores only historical data as needed to successfully meet the requirements of the current transaction.
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