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The grain of a fact table is usually defined at the lowest possible level of detail. This guideline is sometimes relaxed for data marts in an architecture based on Inmon s Corporate Information Factory, since a separate component of that architecture stores atomic data. Even in this architecture, however, fact tables may contain very granular data. Kimball s dimensional data warehouse calls for the star schema to be used as the repository of granular data, and standalone data marts are usually designed to capture detailed information as well. While the data in a fact table is usually stored in a granular format, many reports do not require presentation of the individual details. As you saw in 1, a typical query will aggregate facts to a specified level of dimensional detail. This level of detail is usually controlled in a query by indicating dimensions that will be used to group results. Although the individual atomic measurements do not appear in the final result set, the database must access them in order to compute their sum. Suppose, for example, that an orders star captures order_dollars by order line. Assume that the fact table, shown at the top of Figure 15-1, stores approximately 300,000 rows per month. A simple query that summarizes order_dollars by product, for January 2009, will

q An unsigned applet cannot start any program on the local host. q An unsigned applet cannot talk to a serial or parallel port. q An unsigned applet cannot use the System.setOut() or

require the DBMS to access 300,000 rows from the fact table. This will be necessary regardless of how many products were sold during the period. Even if there were only 200 products sold, the database must use the detailed data to compute their totals.

incorrect geographic location. It is very important that data ranges do not overlap and that a single record can end up only on a single server. The following Create Table statements should be executed on the respective servers:

q An unsigned applet cannot use the Preferences API. q An unsigned applet cannot use the Reflection API.

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Dimensional aggregates store information that has been summarized in advance. They improve query performance by reducing the amount of data that must be accessed and processed by the DBMS. When possible, using the aggregate table instead of the base schema allows the DBMS to spend less time reading data from disks, sorting it, joining rows together, computing sums, and so forth. These reductions in work allow query results to return faster. This also improves overall throughput. Since fewer resources are consumed, they are available to process other queries. Suppose an aggregate fact table is built that includes the same facts about orders as the original schema but at a coarser grain. Depicted in Figure 15-1, order_facts_aggregate summarizes order_dollars by month, product, and salesrep. The original fact table contained roughly 300,000 rows per month; the summary contains roughly 1,000 rows per month.

Given an Architectural System Specification, Identify Appropriate Locations for Implementation of Specified Security Features and Select Suitable Technologies for Implementation of Those Features

DAY ORDER_FACTS day_key product_key salesrep_key customer_key order_id order_line . . . order_dollars . . . SALESREP

q Authentication q Authentication method: BASIC, FORM, DIGEST, and CLIENT-CERT q Digital certificates, certificate authorities q Secure Sockets Layer (SSL) q Common Secure Interoperability (CSIv2) q Identity selection: <run-as> or <use-caller-identity> or @RunAs annotation q Security roles, including @DeclareRoles and @SecurityRoles annotations q Authorization q Authorization enforced by the container (declarative), defined in the de-

The following questions will help you measure your understanding of the material presented in this chapter. Read all the choices carefully because there may be more than one correct answer. Choose all correct answers for each question.

Base Schema Grain: Orders by order line Approximate rows per month: 300,000 Aggregate Schema Grain: Orders by Month, Product and Salesrep Approximate rows per month: 1000 MONTH Conforms with Day PRODUCT ORDER_FACTS_ AGGREGATE month_key product_key salesrep_key . . . order_dollars . . . SALESREP

-- on Canada server CREATE TABLE [dbo].[InventoryCanada] ( [Inventoryid] [int] NOT NULL , [Make] [varchar] (50) NULL , [Model] [varchar] (50) NULL , [Location] [varchar] (50) NULL , [FirstName] [varchar] (30) NULL , [LastName] [varchar] (30) NULL , [AcquisitionType] [varchar] (12) NULL , [Address] [varchar] (50) NULL , [City] [varchar] (50) NULL , [ProvinceId] [char] (3) NULL , [Country] [varchar] (50) NOT NULL , [EqType] [varchar] (50) NULL , [Phone] [typPhone] NULL , [Fax] [typPhone] NULL , [Email] [typEmail] NULL , [UserName] [varchar] (50) NULL , CONSTRAINT [PK_InventoryCanada] PRIMARY KEY

Identify Security Restrictions That Java Technology Environments Normally Impose on Applets Running in a Browser

Summary of order_facts: day: Summarized by month customer: Summarized for all customers order_line: Summarized for all order lines

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