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Any highly available data warehouse will use some form of data replication to tolerate machine failures. In this paper, we demonstrate that we can leverage this data redundancy to build an integrated approach to recovery and high availability. Our approach, called HARBOR, revives a crashed site by querying remote, online sites for missing updates and uses timestamps to determine which tuples need to be copied or updated. HARBOR does not require a stable log, recovers without quiescing the system, allows replicated data to be stored non-identically, and is simpler than a log-based recovery algorithm.We compare the runtime overhead and recovery performance of HARBOR to those of two-phase commit and ARIES, the gold standard for log-based recovery, on a three-node distributed database system. Our experiments demonstrate that HARBOR suffers lower runtime overhead, has recovery performance comparable to ARIES's, and can tolerate the fault of a worker and efficiently bring it back online.