GentleRain is a new causally consistent geo-replicated data store that provides throughput comparable to eventual consistency and superior to current implementations of causal consistency. GentleRain uses a periodic aggregation protocol to deter- mine whether updates can be made visible in accordance with causal consistency. Unlike current implementations, it does not use explicit dependency check messages, result- ing in a major throughput improvement at the expense of a modest increase in update visibility. Furthermore, GentleRain tracks causal consistency by attaching to updates scalar timestamps derived from loosely synchronized physical clocks. Clock skew does not cause violations of causal consistency, but may delay the visibility of updates. By en- coding causality in a single scalar timestamp, GentleRain reduces storage and communication overhead for tracking causality. We evaluate GentleRain using Amazon EC2, and demonstrate that it achieves throughput equal to about 99% of eventual consistency, and 120% better than previous implementations of causal consistency.