A processor-implemented method, comprising: executing, via one or more hardware processors, one or more synthetic benchmarks on a first dataset on a first cluster comprising a first set of nodes, wherein each of said one or more synthetic benchmarks comprises a MapReduce job, said MR job comprises a map task and a reduce task; extracting, via said one or more hardware processors, one or more sensitive parameters related to performance of each sub-phase in said MR job, wherein said one or more sensitive parameters comprises at least one of an input size per map task, map selectivity, number of map tasks, number of reduce tasks, record selectivity, concurrent map tasks per node, one or more map waves, and disk resource contention delay; generating, via said one or more hardware processors, a linear regression prediction model using said one or more sensitive parameters to obtain a set of linear regression prediction models; and generating, via said one or more hardware processors, a performance prediction model based on said set of linear regression prediction models generated using said first dataset; wherein the linear regression prediction model indicates uniform growth in the data distribution, such that each phase of said MR job execution has proportionate increase in input data size with data growth; processing, via said one or more hardware processors, a Hive query such that said Hive query is translated into Directed Acyclic Graph of said MR job; executing, via said one or more hardware processors, said one or more MR jobs on a second dataset on a second cluster comprising a second set of nodes; extracting, via said one or more hardware processors, said one or more sensitive parameters from a job execution log obtained upon executing said DAG of said MR job, wherein the sensitive parameters are related to performance of the sub-phases of said MR job; and predicting, using said performance prediction model, a Hive query execution time of said DAG of said MR job based on the sensitive parameters from execution log obtained for the one or more sub-phases of said MR job executed on said second dataset. executing, via one or more hardware processors, one or more synthetic benchmarks on a first dataset on a first cluster comprising a first set of nodes, wherein each of said one or more synthetic benchmarks comprises a MapReduce job, said MR job comprises a map task and a reduce task; extracting, via said one or more hardware processors, one or more sensitive parameters related to performance of each sub-phase in said MR job, wherein said one or more sensitive parameters comprises at least one of an input size per map task, map selectivity, number of map tasks, number of reduce tasks, record selectivity, concurrent map tasks per node, one or more map waves, and disk resource contention delay; generating, via said one or more hardware processors, a linear regression prediction model using said one or more sensitive parameters to obtain a set of linear regression prediction models; and generating, via said one or more hardware processors, a performance prediction model based on said set of linear regression prediction models generated using said first dataset; wherein the linear regression prediction model indicates uniform growth in the data distribution, such that each phase of said MR job execution has proportionate increase in input data size with data growth; processing, via said one or more hardware processors, a Hive query such that said Hive query is translated into Directed Acyclic Graph of said MR job; executing, via said one or more hardware processors, said one or more MR jobs on a second dataset on a second cluster comprising a second set of nodes; extracting, via said one or more hardware processors, said one or more sensitive parameters from a job execution log obtained upon executing said DAG of said MR job, wherein the sensitive parameters are related to performance of the sub-phases of said MR job; and predicting, using said performance prediction model, a Hive query execution time of said DAG of said MR job based on the sensitive parameters from execution log obtained for the one or more sub-phases of said MR job executed on said second dataset.