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There may be other facts about the discography business, but for the sake of keeping this example simple, let s agree that they re not important right now. In order to implement the logical model in our solution, we need to transform it into a physical model. This transformation is a science of its own, and is as such outside the scope of this chapter. Therefore we ll now move straight to the finished physical model, shown in figure 1. The bold outlined boxes represent our entities along with their attributes. The arrows and the light outlined boxes represent the relationships: the one-to-many (for example, several Albums can have the same Title) and the many-to-many relationships (for example, several Persons can be members of several Bands), respectively. The callouts connected to each box list the attributes of each individual entity, and also contain other information specifically the constraints that need to be implemented. We can see that one new entity has been added to the physical model, named Title, and that it s referenced by both the Album as well as the Track entity. This new entity

A partition roll-in and roll-out strategy deals with deleting the old table partitions out of the database when they are no longer required (roll-out) and adding the new data that is loaded to new table partitions (roll-in) Using this strategy, you can load the new data on the partitions that reside on the Exadata Storage Server, while the old data on the legacy storage will be deleted over time, based on the roll-out criteria This method does not incur a data migration from the source to the destination database However, the main requirement for using this method is that the data model should be designed for the partition roll-in/roll-out strategy The requirement seems stringent for any realistic migration use-case to fit in, but it might be possible to use this method in conjunction with other methods, like the Data Pump, which will move part of the data that does not support the partition roll-in/roll-out rules We will discuss the Data Pump method in later sections The considerations for using this method are

uq(Title)

At Audio Frequencies Transformers for use at AF are similar to those employed for 60-Hz electricity The differences are that the frequency is somewhat higher (up to 20 kHz), and that audio signals exist in a band of frequencies (20 Hz to 20 kHz) rather than at only one frequency Most AF transformers are constructed like miniature utility transformers They have laminated E cores with primary and secondary windings wound around the crossbars, as shown in Fig 18-4 Audio transformers can be either the step-up or the step-down type However, rather than being made to produce a specific voltage, AF transformers are designed to match impedances Audio circuits, and in fact all electronic circuits that handle sine-wave or complex-wave signals, exhibit impedance at the input and output The load has a certain impedance; a source has another impedance Good audio design strives to minimize the reactance in the circuitry, so that the absolute-value impedance Z is close to the resistance R This means that X must be zero or nearly zero In the following discussion of impedance-matching transformers, for both AF and RF applications, assume that the reactance is zero, so the impedance is purely resistive with Z = R + j 0

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The source system platform should be based on Linux x86-64 and the database should be 112 The use of ASM on the source database is optional The new table partitions will reside in the ASM on the Database Machine

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Transformers can provide isolation between electronic circuits While there is inductive coupling in a transformer, there is comparatively little capacitive coupling The amount of capacitive coupling can be reduced by using cores that minimize the number of wire turns needed in the windings, and by keeping the windings physically separated from each other (rather than overlapping)

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The legacy storage needs to be connected to the Database Machine using NFS or iSCSI Data migration is not needed in this option There will be a slight downtime associated in order to switch the database and ASM instances from the non-Exadata platform to the Database Machine The source database data model should support partition roll-out and roll-in requirements as stated earlier Smart Scans across the new partitions residing on the Exadata Storage Servers and the old partitions residing on the legacy storage are not possible The legacy storage cannot be decommissioned quickly, and this could be an issue when justifying the purchase of the Database Machine

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The Data Guard physical standby database is a block-for-block replica of the primary database The Data Guard apply process is responsible for replicating the changes from the redo logs on the primary database to the physical standby database in real time, and it supports both synchronous and asynchronous mechanisms Physical standby databases can be used for performing database platform migrations The method involves creating a Data Guard physical standby database of the current source database on the Database Machine and then performing a Data Guard switchover to the physical standby The considerations for using this method are