The load utilities for data warehou

The load utilities for data warehouses have to deal with much
larger data volumes than for operational databases. There is
only a small time window (usually at night) when the
warehouse can be taken offline to refresh it. Sequential loads
can take a very long time, e.g., loading a terabyte of data can
take weeks and months! Hence, pipelined and partitioned
parallelism are typically exploited 6. Doing a full load has the
advantage that it can be treated as a long batch transaction
that builds up a new database. While it is in progress, the
current database can still support queries; when the load
transaction commits, the current database is replaced with the
new one. Using periodic checkpoints ensures that if a failure
occurs during the load, the process can restart from the last
checkpoint.

ผลลัพธ์ (อังกฤษ) 2:

Load the utilities for Data warehouses have much to Deal with
Data Volumes larger than for operational databases. There is
only a Small Window time (usually at Night) when the
Warehouse Can be taken offline to refresh it. Sequential loads
Can take a very long time, eg, a terabyte of Loading Data Can
take Months and weeks! Hence, pipelined and partitioned
parallelism are typically exploited 6. Doing a full Load has the
Advantage that Can it be treated As a long Batch Transaction
that builds up a New Database. While it is in Progress, the
current Database Support Can still queries; when the Load
Transaction commits, the current Database is replaced with the
New one. Using periodic checkpoints ensures that if a Failure
occurs during the Load, the Process Can restart from the last
Checkpoint.