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Large-scale datasets collected from heterogeneous sources often require a join operation to extract valuable information. MapReduce is an efficient programming model for large-scale data processing. However, it has some limitations in processing heterogeneous datasets. In this study, we review the state-of-the-art strategies for joining two datasets based on an equi-join condition and provide a detail implementation for each strategy. We also present an in-depth analysis of the join strategies and discuss their feasibilities and limitations to assist the reader in selecting the most efficient strategy. Concluding, we outline interesting directions for future join processing systems.