Data-intensive scalable computing (DISC) frameworks, such as Apache Spark, support runtimes in many popular languages. Yet, SQL is still the most commonly used front-end language for DISC applications due to its broad presence in new and legacy workflows and shallow learning curve. However, DISC-backed SQL introduces several layers of abstraction that significantly reduce the visibility and transparency of workflows, making it challenging for developers to find and fix errors in a query. When a query returns incorrect outputs, it takes a non-trivial, manual effort to comprehend every stage of the query execution and find the root cause of bugs among the input data and complex SQL query. We aim to bring the benefits of step-through interactive debugging to DISC-powered SQL with DeSQL. When a SQL query is executed on a DISC system, DeSQL automatically decomposes it into subqueries and closely monitors the execution to identify the precise intermediate data corresponding to every constituent subquery. This enables a complete interactive debugging experience with full access to the intermediate query states. We evaluate DeSQL’s scalability, overhead, and efficiency against two baselines. The experiment results show that DeSQL can provide a complete debugging view in 13% less time than the original job time while incurring an average overhead of 10% in addition to retaining Apache Spark’s scale-out and scale-up properties. Through a user study comprising 10 participants engaged in two debugging tasks, we find that participants utilizing DeSQL identify the root cause behind a wrong query output in 75% less time than the de-facto, manual debugging.