Modern enterprises store vast, heterogeneous data across large data lakes, where the central challenge is shifting from answering an isolated question to discovering and assembling the tables and documents that together serve an analytical goal. We refer to such a coherent collection of related assets as a data product. Existing table-text benchmarks evaluate factoid question answering over individual tables and therefore do not capture this discovery and assembly problem. We introduce DPDisc, the first benchmark for data product discovery: given a high-level Data Product Request (DPR), a system must retrieve the coherent set of tables and passages that satisfy it. DPDisc is constructed by DPForge, an automated pipeline that repurposes table-text QA datasets by clustering related tables and passages into products, generating DPRs with an LLM ensemble, and validating them through multi-phase LLM evaluation. It comprises 13,076 validated instances with full provenance, spanning open-domain and financial sources. Sparse, dense, and hybrid baselines establish that the task is feasible yet reveal large performance gaps across domains, motivating structure-aware retrieval.