With the increasing popularity of serving and storing data in multiple data centers, we investigate the efficiency of majority quorum-based data consistency algorithms under this scenario. Because of the failure-prone nature of distributed storage systems, majority quorum-based data consistency algorithms become one of the most widely adopted approaches. In this paper, we propose the MeteorShower framework, which provides faulttolerant read/write key-value storage service across multiple data centers with sequential consistency guarantees. A major feature is that most read operations are executed locally within a single data center. This results in lowering read latency from hundreds of milliseconds to tens of milliseconds. The data consistency algorithm in MeteorShower augments majority quorum-based algorithms. Thus, it keeps all the desirable properties of majority quorums, such as fault tolerance, balanced load, etc. An implementation of MeteorShower on top of Cassandra is deployed and evaluated in multiple data centers using the Google Cloud Platform. Evaluations of MeteorShower framework have shown that it can consistently serve read requests without paying the communication delays among replicas maintained in multiple data centers. As a result, we are able to improve the latency of read requests from hundreds of milliseconds to tens of milliseconds while achieving the same latency on write requests and the same fault tolerance guarantee. Thus, MeteorShower is optimized for read intensive workloads.