In emerging systems (e.g., social media and e-commerce platforms), data records are often drawn from heterogeneous sources, such as relational tables, text documents, image repositories, spatial databases, and knowledge graphs. Accordingly, retrieving target records for question-answering (QA) tasks requires us to jointly exploit these heterogeneous sources. However, most existing benchmarks are constructed from individual sources, and only a very few recent benchmarks have considered two or three sources. To alleviate this issue, we introduce HETERQA, a comprehensive benchmark with 857 QA pairs for record retrieval over five heterogeneous sources. HETERQA instantiates this setting with Yelp business records, each of which is grounded by multiple sources. We build HETERQA in an answer-driven manner: candidate records are first initialized with record-field constraints, then enriched through heterogeneous sources, and finally cross-verified across required sources before the natural-language question is retained. We validate the benchmark through contradiction detection and human validation, and further evaluate sparse, dense, hybrid, late-interaction, and agentic retrievers under the same metrics. The results show that HETERQA is challenging: hybrid retrieval achieves the strongest Recall@10, Self-RAG achieves the best MRR@10, and all evaluated methods remain far from saturating the benchmark. These findings indicate that HETERQA provides an effective testbed for record retrieval over heterogeneous sources and leaves substantial room for future retrieval methods. The benchmark dataset and source code are publicly available at https://huggingface.co/datasets/hanchang02/HeterQA and https://github.com/hanchang02/HeterQA, respectively.

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