Data warehouses collect large quantities of data from distributed
sources into a single repository.  A typical load to create or
maintain a warehouse processes GBs of data, takes hours or even days
to execute, and involves many complex and user-defined transformations
of the data (e.g., find duplicates, resolve data inconsistencies, and
add unique keys).  If the load fails, a possible approach is to
``redo'' the entire load.  A better approach is to resume the
incomplete load from where it was interrupted.  Unfortunately,
traditional algorithms for resuming the load either impose
unacceptable overhead during normal operation, or rely on the
specifics of simple transformations. We develop a resumption algorithm
called DR that imposes no overhead and relies only on the basic
properties of the transformations.  We show experimentally
that DR can lead to almost a ten-fold reduction in resumption time.