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A data warehouse can be defined as providing a repository of trusted historical data in a form especially useful and optimized for reporting and performing ad hoc queries and analyses Examples of analyses include trending, forecasting, and mathematical modeling where outcomes are influenced by a complex set of variables A data warehousing system differs from an online transaction processing (OLTP) system, in that the data held in the data warehouse is nonvolatile and generally retained for long periods The data is cleansed of errors and duplication during an extraction, transformation, and loading (ETL) process The data warehouse sometimes incorporates sources of data outside of the company or organization to augment internal data sources Data warehousing has its roots in what were once called decision support systems that gained popularity in the late 1970s.

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often have a very conservative approach to investing, so this must be taken in to account when they decide what options strategies to apply to a portfolio When using options with this investment objective, you must keep in mind that options by nature are limited because they cannot be held inde nitely Options must have an expiration date; therefore, as a risk management strategy they are somewhat short term in nature By short term we mean, even long-term expiration options are three years or less This does not mean that options cannot be used in a portfolio with the above objective Quite the opposite is true Options can help build signi cant value by capturing premium through writing options, limiting risk during periods of instability to preserve portfolio value, and de ning risk on higher risk and aggressive positions.

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Long-term investment objectives are an ideal situations for adding option trading to the portfolio If the portfolio has more of a xed long-term structure and goals, many option strategies are open for the long-term bene t of the portfolio Investors with this objective are often viewed as averse to risk and often reject options based on the common view that options are risky, however, with the right strategy option trading can improve risk management A lifestyle type of investor often has a much different objective The investor is likely to be more willing to speculate with the objective to make my life better today and tomorrow or to simply increase my wealth A lifestyle type of investor is going to expect a more signi cant return on investment in a much shorter period of time.

While OLTP systems predate this concept, the initial design point of transaction processing systems is to provide a repository for current and frequently updated data As a result, the primary focus of OLTP systems is to report on the current business situation But as organizations gather more and more data, business strategists envision new usages of such data to better understand historical business trends and predict future trends and business results Thus was born the need for a separate repository flexible enough to handle this rather different workload with the ability to grow and support extremely large amounts of data Initially, the schema (the collection of tables, views, indexes, and synonyms) was most often deployed with third normal form design for a data warehouse, the same type of schema design used for OLTP databases.

This type is not necessarily a sophisticated or quali ed investor, so you must be clear on the risk suitability for investing with options Volatility is going to be much more of a signi cant factor because frequently smaller, less sophisticated investors have monetary risk.

The Oracle Exadata Database Machine uses Oracle Enterprise Linux (OEL) as the operating system on both the database server nodes and the Exadata Storage Server. In 2011, Oracle also began supporting Solaris as an alternative operating system you might deploy on the database server nodes. Although you cannot modify the software or configuration of OEL on the Exadata

In such a schema, tables contain only detailed data, stored only once, and nonprimary attributes are linked among tables using foreign keys Figure 7-1 illustrates such a schema containing customer, order, product, and shipping information The third normal form schema is ideal where the goal is to provide fast and.

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