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In most environments, it is not possible to use only Data Pump There are two main reasons for this: first, Data Pump can only work with releases 10g and 11g When it is necessary to transfer data between these releases and an older database, Export/Import is the only tool Second, there is one thing that Export/Import can do but Data Pump cannot do: write to the operating system of the user process This is a critical architectural difference: Export/Import are user processes that use files local to the user; Data Pump is a server-side facility that uses files on the database server The Export/Import utilities are not tested in the OCP examinations, but all DBAs should be familiar with them

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Data Pump is a server-side utility You initiate Data Pump jobs from a user process, either SQL*Plus or through Enterprise Manager, but all the work is done by server processes This improves performance dramatically over the old Export/Import utilities, because the Data Pump processes running on the server have direct access to the datafiles and the SGA; they do not have to go via a session Also, it is possible to launch a Data Pump job and then detach from it, leaving it running in the background You can reconnect to the job to monitor its progress at any time There are a number of processes involved in a Data Pump job, two queues, a number of files, and one table First, the processes: The user processes are expdp and impdp (for Unix) or expdpexe and impdpexe (Windows) These are used to launch, control, and monitor Data Pump jobs Alternatively, there is an Enterprise Manager interface, more on which later The expdp or impdp user process establishes a session against the database through a normal server process This session then issues commands to control and monitor Data Pump jobs When a Data Pump job is launched, at least two processes are started: a Data Pump Master process (the DMnn) and one or more worker processes (named DWnn) If multiple Data Pump jobs are running concurrently, each will have its own DMnn process, and its own DWnn processes As the name implies, the master process controls the workers If you have enabled parallelism, then each DWnn may make use of two or more parallel execution servers (named Pnnn) Two queues are created for each Data Pump job: a control queue and a status queue The DMnn divides up the work to be done and places individual tasks that make up the job on the control queue The worker processes pick up these tasks and execute them perhaps making use of parallel execution servers This queue operates on a deliver-exactly-once model: messages are enqueued by the DMnn and dequeued by the worker that picks them up The status queue is for monitoring.

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