Data-intensive scalable computing has become popular due to the increasing demands of analyzing big data. For example, Apache Spark and Hadoop allow developers to write dataflow-based applications with user-defined functions to process data with custom logic. Testing such applications is difficult. (1) These applications often take multiple datasets as input. (2) Unlike in SQL, there is no explicit schema for these datasets and each unstructured (or semi-structured) dataset is segmented and parsed at runtime. (3) Dataflow operators (e.g., join) create implicit co-dependence constraints between the fields of multiple datasets. An efficient and effective testing technique must analyze co-dependence among different regions of multiple datasets at the level of rows and columns and orchestrate input mutations jointly on co-dependent regions. We propose CoFuzz to increase the effectiveness and efficiency of fuzz testing dataflow-based big data applications. The key insight behind CoFuzz is two folds. It keeps track of which code segments operate on which datasets, which rows, and which columns. By analyzing the use of dataflow operators (e.g., join and groupBy) in tandem with the semantics of UDFs, CoFuzz generates test data that subsequently reach hard-to-reach regions of the application code. In real-world big data applications, CoFuzz finds 3.4× more faults, achieving 29% more statement coverage in half the time as Jazzer’s, a state-of-the-art commercial fuzzer for Java bytecode. It outperforms prior DISC testing by exposing deeper semantic faults beyond simpler input formatting errors, especially when multiple datasets have complex interactions through dataflow operators.