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You must back up your msdb, master, and model system databases. In addition, if your server is configured as a replication distributor, you need to back up the distribution database. Back ups of these system databases enable you to restore and recover the SQL Server system in the event of system failure (for example, a hard disk failure).



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When this MyBean backing instance is created, a persistence context is also created for the injected manager field. The persistence context has the same lifespan as the bean. When the stateful session bean is removed, the persistence context is closed. This means that any entity object instances remain attached and managed as long as the stateful session bean is active.

newRow3.Item("CategoryName") = "Seafood"

How volatile is the data Data is said to be volatile when it changes frequently. As a general rule, the more volatile the data, the more regularly you should back it up. For example, readonly databases do not normally change and therefore do not need to be backed up regularly. A database whose data changes once a day, however, should be backed up daily. And a database whose data is continually changing needs to backed up repeatedly throughout the day.





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It is strongly suggested that you use the @PersistenceContext annotation or the XML equivalent when using Java Persistence with EJBs. These features were defined to make it easier for developers to interact with entity beans. Entity managers created using EntityManagerFactory are more error-prone because the application developer has to worry about more things. For instance, the developer could forget to close() an entity manager and subsequently leak resources. Take advantage of the ease-of-use facilities of your EJB container!

DataSet1.Tables("Categories").Rows.Add(newRow3)

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How much of the data can the organization afford to lose Some data, such as that used only in testing and development, is naturally less valuable to an organization than other data, such as that used in production. A database administrator must stay informed about the shifting importance of an organization s database applications to ultimately determine the relative value of database data. For the purpose of designing a backup strategy, the amount of data that the orga nization can afford to lose is often best measured in time, not in bytes. In other words, when you determine how much data within a given database your orga nization can afford to lose, it is often useful to specify the maximum acceptable span, such as an hour or day, during which time updates made to that database can be lost. For example, if a database is damaged at 11:30, determine whether the recovery should enable you to restore the database to the state the database was in at 11:00 the same day, midnight the night before, or some earlier point. The trade-off for ensuring a brief window of data loss is the corresponding need to perform more frequent backups, which use more resources. Another trade-off is that backup strategies that require more frequent backups require a longer process of disaster recovery.

Now that you have learned how to deploy and obtain a reference to an entity manager, you are ready to learn the semantics of interacting with it. The EntityManager API has methods to insert and remove entities from a database as well as merge updates from detached entity instances. There is also a rich query API that you can access by creating query objects from certain EntityManager methods:

In general, the more mission-critical or volatile a given set of data, the shorter the period of acceptable data loss. And the less mission-critical or volatile a given set of data, the longer the period of acceptable data loss.

// C# DataRow newRow = dataSet1.Tables["Categories"].NewRow();

When asked how much data loss is acceptable, any DBA will say that business users always say zero. But zero data loss is just not a possibility. To illustrate: One data center we know was hit by powerful lightning strikes many times dur ing a storm. These lightning bolts fried not only several UPS units but also destroyed several servers. Even though backups were regularly shipped offsite, there was no way to protect current data in such a case. Remember that, regardless of your backup strategy, there is always the possibility of a disaster that will result in some data loss.

package javax.persistence; import import import import java.util.Map; javax.persistence.metamodel.Metamodel; javax.persistence.criteria.CriteriaBuilder; javax.persistence.criteria.CriteriaQuery;

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