The areal density growth of hard disk drives is hindered by the limits imposed by the laws of physics and any further significant growth to the density demands some major changes to the currently employed recording techniques. Shingled Magnetic Recording (SMR) is leading next generation disk technology. SMR disks employ a shingled write process that overlaps the data tracks on the disk surface like the shingles on a roof, thereby increasing disk areal density with minimal manufacturing changes. While these disks have the same read behavior as current disks, random writes and in-place data updates are no longer possible, since a write to a track must overwrite and destroy data on all tracks that it overlaps. Given this change in write behavior, we argue that the best way to utilize these disks is not by masquerading them as traditional disks, but by using approaches that leverage their proclivity for sequential writes. This thesis proposes a key-value object interface for SMR disks, and presents SMRDB, a Log-Structured Merge (LSM) tree based key-value data store for SMR disks, that despite being restricted to sequential writes outperforms a state-of-the-art LSM-tree based key-value store in a conventional setting, demonstrating that SMR disks can be effectively used to replace conventional disks for many applications. In the second part of the thesis, to measure the impact compaction has on incoming requests and the effectiveness of compaction overhead mitigation techniques under realistic loads, we show that the commonly observed temporal characteristics can be modeled using three categories of arrival processes: a)Poisson, b)Self similar, and c)Envelope-guided processes, and have incorporated all three models into the Yahoo! Cloud Serving Benchmark. In the final part of this thesis, we show that the negative impact that compaction would have on performance can be mitigated in a hybrid SMR drive configuration, by write-offloading to the NVRAM component of the hybrid drive, when and only when compaction is in progress.