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Oracle 11g Sql Chapter 4 Answers

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Oracle 11g Sql Chapter 4 Answers

In this chapter, we learned about the role that data and databases play in the context of information systems. Data is made up of small facts and information without context. If you give data context, then you have information. Knowledge is gained when information is consumed and used for decision making. A database is an organized collection of related information. Relational databases are the most widely used type of database, where data is structured into tables and all tables must be related to each other through unique identifiers. A database management system (DBMS) is a software application that is used to create and manage databases, and can take the form of a personal DBMS, used by one person, or an enterprise DBMS that can be used by multiple users. A data warehouse is a special form of database that takes data from other databases in an enterprise and organizes it for analysis. Data mining is the process of looking for patterns and relationships in large data sets. Many businesses use databases, data warehouses, and data-mining techniques in order to produce business intelligence and gain a competitive advantage.

In this chapter, we will focus on using SQL to create the database and table structures, mainly using SQL as a data definition language (DDL). In Chapter 16, we will use SQL as a data manipulation language (DML) to insert, delete, select and update data within the database tables.

This chapter deals with simple group operations involving theaggregate functions, the GROUP BY and HAVING clauses. Advanced groupoperations such as ROLLUP, CUBE, and GROUPING SETS are discussed inChapter 13.

As these examples show, using the Spark SQL interface to query data is similar to writing a regular SQL query to a relational database table. Although the queries are in SQL, you can feel the similarity in readability and semantics to DataFrame API operations, which you encountered in Chapter 3 and will explore further in the next chapter.

Writing or saving a DataFrame as a table or file is a common operation in Spark. To write a DataFrame you simply use the methods and arguments to the DataFrameWriter outlined earlier in this chapter, supplying the location to save the Parquet files to. For example:

Appenders are named entities. This ensures that they can be referenced by name, a quality confirmed to be instrumental in configuration scripts. The Appender interface extends the FilterAttachable interface. It follows that one or more filters can be attached to an appender instance. Filters are discussed in detail in a subsequent chapter.

The ConsoleAppender, as the name indicates, appends on the console, or more precisely on System.out or System.err, the former being the default target. ConsoleAppender formats events with the help of an encoder specified by the user. Encoders will be discussed in a subsequent chapter. Both System.out and System.err are of type Consequently, they are wrapped inside an OutputStreamWriter which buffers I/O operations.

A sample application, chapters.appenders.mail.EMailgenerates a number of log messages followed by a singleerror message. It takes two parameters. The first parameter is aninteger corresponding to the number of logging events togenerate. The second parameter is the logback configurationfile. The last logging event generated by EMailapplication, an ERROR, will trigger the transmission of an emailmessage.

Once data have been collected and linked, it is necessary to store and organize them. Many social scientists are used to working with one analytical file, often in SAS, Stata, SPSS, or R. But most organizations store (or should store) their data in databases, which makes it critical for social scientists to learn how to create, manage, and use databases for data storage and analysis. This chapter describes the concept of databases and introduces different types of databases and analysis languages (in particular, relational da

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