The ubiquity of location enabled devices has resulted in a wide proliferation of location based applications and services. To handle the growing scale, database management systems driving such location based services (LBS) must cope with high insert rates for location updates of millions of devices, while supporting efficient real-time analysis on latest location. Traditional DBMSs, equipped with multi-dimensional index structures, can efficiently handle spatio-temporal data. However, popular open source relational database systems are overwhelmed by the high insertion rates, real-time querying requirements, and terabytes of data that these systems must handle. On the other hand, Key-value stores can effectively support large scale operation, but do not natively support multi-attribute accesses needed to support the rich querying functionality essential for the LBSs. We present MD-HBase, a scalable data management system for LBSs that bridges this gap between scale and functionality. Our approach leverages a multi-dimensional index structure layered over a Key-value store. The underlying Key-value store allows the system to sustain high insert throughput and large data volumes, while ensuring fault-tolerance, and high availability. On the other hand, the index layer allows efficient multi-dimensional query processing. We present the design of MD-HBase that builds two standard index structures - "the K-d tree and the Quad tree - "over a range partitioned Key-value store. Our prototype implementation using HBase, a standard open-source Key-value store, can handle hundreds of thousands of inserts per second using a modest 16 node cluster, while efficiently processing multidimensional range queries and nearest neighbor queries in real-time with response times as low as hundreds of milliseconds.