Data Analytics has become very popular on large datasets in different organizations. It is inevitable to use distributed resources such as Clouds for Data Analytics and other types of data processing at larger scales. To effectively utilize all system resources, an efficient scheduler is needed, but the traditional resource managers and job schedulers are centralized and designed for larger batch jobs which are fewer in number. Frameworks such as Hadoop and Spark, which are mainly designed for Big Data analytics, have been able to allow for more diversity in job types to some extent. However, even these systems have centralized architectures and will not be able to perform well on large scales and under heavy task loads. Modern applications generate tasks at very high rates that can cause significant slowdowns on these frameworks. Additionally, over-decomposition has shown to be very useful in increasing the system utilization. In order to achieve high efficiency, scalability, and better system utilization, it is critical for a modern scheduler to be able to handle over-decomposition and run highly granular tasks. Further, to achieve high performance, Albatross is written in C/C++, which imposes a minimal overhead to the workload process as compared to languages like Java or Python. We propose Albatross, a task level scheduling and execution framework that uses a Distributed Message Queue (DMQ) for task distribution among its workers. Unlike most scheduling systems, Albatross uses a pulling approach as opposed to the common push approach. The former would let Albatross achieve a good load balancing and scalability. Furthermore, the framework has built in support for task execution dependency on workflows. Therefore, Albatross is able to run various types of workloads, including Data Analytics and HPC applications. Finally, Albatross provides data locality support. This allows the framework to achieve higher performance through minimizing the amount of unnecessary da...