  Big Data is reforming many industrial domains by providing decision support
through analyzing large data volumes. Big Data testing aims to ensure that Big
Data systems run smoothly and error-free while maintaining the performance and
quality of data. However, because of the diversity and complexity of data,
testing Big Data is challenging. Though numerous research efforts deal with Big
Data testing, a comprehensive review to address testing techniques and
challenges of Big Data is not available as yet. Therefore, we have
systematically reviewed the Big Data testing techniques evidence occurring in
the period 2010-2021. This paper discusses testing data processing by
highlighting the techniques used in every processing phase. Furthermore, we
discuss the challenges and future directions. Our findings show that diverse
functional, non-functional and combined (functional and non-functional) testing
techniques have been used to solve specific problems related to Big Data. At
the same time, most of the testing challenges have been faced during the
MapReduce validation phase. In addition, the combinatorial testing technique is
one of the most applied techniques in combination with other techniques (i.e.,
random testing, mutation testing, input space partitioning and equivalence
testing) to find various functional faults through Big Data testing.
