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This work demonstrates that the solution space of these challenges has expanded with deep learning now moving beyond traditional applications in computer vision and natural language processing to diverse and core machine learning tasks such as learning with streaming and non-iid-data, partial supervision, and large volumes of distributed data while preserving privacy. We present a framework coalescing multiple deep methods and corresponding models as responses to specific Big Data challenges. First, we perform a detailed per-challenge review of existing techniques, with benchmarks and usage advice, and subsequently synthesize them together into one organic construct that we discover principally uses extensions of one underlying model, the autoencoder. This work therefore provides a synthesis where challenges at scale across the Vs of Big Data could be addressed by new algorithms and architectures being proposed in the deep learning community. The value being proposed to the reader from either community in terms of nomenclature, concepts, and techniques of the other would advance the cause of multi-disciplinary, transversal research and accelerate the advance of technology in both domains.<\/jats:p>","DOI":"10.1145\/3427476","type":"journal-article","created":{"date-parts":[[2021,1,2]],"date-time":"2021-01-02T17:08:21Z","timestamp":1609607301000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Potential Deep Learning Solutions to Persistent and Emerging Big Data Challenges\u2014A Practitioners\u2019 Cookbook"],"prefix":"10.1145","volume":"54","author":[{"given":"Behroz","family":"Mirza","sequence":"first","affiliation":[{"name":"National University of Computer and Emerging Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0638-9689","authenticated-orcid":false,"given":"Tahir Q.","family":"Syed","sequence":"additional","affiliation":[{"name":"Institute of Business Administration"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Behraj","family":"Khan","sequence":"additional","affiliation":[{"name":"National University of Computer and Emerging Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yameen","family":"Malik","sequence":"additional","affiliation":[{"name":"Seedcode inc."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,1,2]]},"reference":[{"volume-title":"Proceedings of the USENIX Symposium on Operating Systems Design and Implementation. 265--283","author":"Abadi M.","key":"e_1_2_1_1_1"},{"volume-title":"Proceedings of the International Conference on Artificial Intelligence and Statistics. 1--8.","year":"2010","author":"Adams Ryan","key":"e_1_2_1_2_1"},{"key":"e_1_2_1_3_1","unstructured":"National Security Agency. 2013. 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