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In many organizations, you will be working with different types of databases daily, and thus with different types of data and management requirements. You may find yourself working on simple SQL queries with users and simultaneously wrestling with decision-support systems for management. Databases perform a variety of functions, but you can group all of those functions into two broad categories: online transaction processing (OLTP) and decision-support systems (DSSs; sometimes also called online analytical processing, or OLAP). Let s take a quick look at some of the basic classifications of Oracle databases.

Online transaction processing databases are the bread and butter of most consumer- and supplieroriented databases This category includes order entry, billing, customer, supplier, and supply-chain databases These databases are characterized by heavy transaction volume and a need to be online continuously, which today (given the use of the Internet to access such systems) means 24/7/365 availability, short maintenance intervals, and low tolerance for breakdowns in the system Decision-support systems range from small databases to large data warehouses These are typically not 24/7 operations, and they can easily manage with regularly scheduled downtime and maintenance windows The extremely large size of some of these data warehouses necessitates the use of special techniques both to load and to use the data There isn t a whole lot of difference between the administration of a DSS-oriented data warehouse and a transaction-oriented OLTP system from the DBA s perspective.

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Office in Virginia 06530,Consulting,Virginia,This is the Consulting Office in Virginia 05940,Finance,Virginia,This is the Finance Office in Virginia [tkyte@desktop tkyte]$ we can load it using that control file. In our input data file, we have four rows of data. The first row starts with 055, meaning that the next 55 bytes represent the first input record. This 55 bytes includes the terminating newline after the word Virginia. The next row starts with 065. It has 65 bytes of text, and so on. Using this format data file, we can easily load our data with embedded newlines. Again, if you are using UNIX and Windows (the preceding example was with UNIX, where a newline is one character long), you would have to adjust the length field for each record. On Windows, the preceding example s .dat file would have to contain 56, 66, 66, and 60 for the values in the length fields.

The cursor sharing removed information from the query. It found every literal, including the parameters to the built-in substring function, which were constants we were using. It removed them from the query and replaced them with bind variables. The SQL engine no longer knows that the column is a substring of length 1 it is of indeterminate length. Also, you can see that where rownum = 1 is now bound as well. That seems like a good idea; however, the optimizer has just had some important information removed. It no longer knows that this query will retrieve a single row ; it now believes this query will return the first N rows and N could be any number at all. In fact, if you run these queries with SQL_TRACE=TRUE, you will find the query plans used by each query and the amount of work they perform to be very different. Consider the following: select /* TAG */ substr( username, 1, 1 ) from all_users au1 where rownum = 1

The backup and recovery methodology is essentially the same, and database security and other related issues are also very similar The big difference between the two types of databases occurs at the design and implementation stages DSS systems usually involve a different optimization strategy for queries and different physical storage strategies Oracle Database 11g provides you with the choice of implementing an OLTP database or a DSS database using the same database server software Performance design considerations that may work well with one type of database may be entirely inappropriate for another type of database For example, a large number of indexes can help you query a typical data warehouse efficiently while you are getting some reports out of that database.

If you have the same number of indexes on a live OLTP system with a large number of concurrent users, you may see a substantial slowing down of the database, because the many updates, inserts, and deletes on the OLTP system require more work on the part of the database..

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