Today, many organizations need to operate on datathat is distributed around the globe. This is inevitable due to thenature of data that is generated in different locations such as videofeeds from distributed cameras, log files from distributed servers, and many others. Although centralized cloud platforms havebeen widely used for data-intensive applications, such systemsare not suitable for processing geo-distributed data due to highdata transfer overheads. An alternative approach is to use anEdge Cloud which reduces the network cost of transferringdata by distributing its computations globally. While the EdgeCloud is attractive for geo-distributed data-intensive applications, extending existing cluster computing frameworks to a wide-areaenvironment must account for locality. We propose Awan: anew locality-aware resource manager for geo-distributed dataintensiveapplications. Awan allows resource sharing betweenmultiple computing frameworks while enabling high localityscheduling within each framework. Our experiments with theNebula Edge Cloud on PlanetLab show that Awan achieves up toa 28% increase in locality scheduling which reduces the averagejob turnaround time by approximately 18% compared to existingcluster management mechanisms.