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Cloud data management systems are growing in prominence, particularly at large Internet companies like Google, Yahoo!, and Amazon, which prize them for their scalability and elasticity. Each of these systems trades off between low-latency serving performance and batch processing throughput. In this paper, we discuss our experience running batch-oriented Hadoop on top of Yahoo's serving-oriented PNUTS system instead of the standard HDFS file system. Though PNUTS is optimized for and primarily used for serving, a number of applications at Yahoo! must run batch-oriented jobs that read or write data that is stored in PNUTS. Combining these systems reveals several key areas where the fundamental properties of each system are mismatched. We discuss our approaches to accommodating these mismatches, by either bending the batch and serving abstractions, or inventing new ones. Batch systems like Hadoop provide coarse task-level recovery, while serving systems like PNUTS provide finer record or transaction-level recovery. We combine both types to log record-level errors, while detecting and recovering from large-scale errors. Batch systems optimize for read and write throughput of large requests, while serving systems use indexing to provide low latency access to individual records. To improve latency-insensitive write throughput to PNUTS, we introduce a batch write path. The systems provide conflicting consistency models, and we discuss techniques to isolate them from one another.