  The paper aims to find an efficient way for processing large datasets having
different types of workload queries with minimal replication. The work first
identifies the complexity of queries best suited for the given data processing
tool . The paper proposes Query Complexity Aware partitioning technique QCA
with a lightweight query identification and partitioning algorithm. Different
replication approaches have been studied to cover more use-cases for different
application workloads. The technique is demonstrated using a scientific dataset
known as Sloan Digital Sky Survey SDSS. The results show workload execution
time WET reduced by 94.6% using only 6.7% of the dataset in loaded format
compared to the original dataset. The QCA technique also reduced multi-node
replication by 5.8x times compared to state-of-the-art workload aware WA
techniques. The multi-node and multi-core execution of workload using QCA
proposed partitions reduced WET by 42.66% and 25.46% compared to WA.
