  Erasure codes are an integral part of many distributed storage systems aimed
at Big Data, since they provide high fault-tolerance for low overheads.
However, traditional erasure codes are inefficient on reading stored data in
degraded environments (when nodes might be unavailable), and on replenishing
lost data (vital for long term resilience). Consequently, novel codes optimized
to cope with distributed storage system nuances are vigorously being
researched. In this paper, we take an engineering alternative, exploring the
use of simple and mature techniques -juxtaposing a standard erasure code with
RAID-4 like parity. We carry out an analytical study to determine the efficacy
of this approach over traditional as well as some novel codes. We build upon
this study to design CORE, a general storage primitive that we integrate into
HDFS. We benchmark this implementation in a proprietary cluster and in EC2. Our
experiments show that compared to traditional erasure codes, CORE uses 50% less
bandwidth and is up to 75% faster while recovering a single failed node, while
the gains are respectively 15% and 60% for double node failures.
