{ "ok": true, "doi": "10.46243/jst.2021.v6.i04.pp11-14", "doi_display": "10.46243/jst.2021.v6.i04.pp11-14", "doi_url": "https://doi.org/10.46243/jst.2021.v6.i04.pp11-14", "state": "registered", "url": "https://www.jst.org.in/index.php/pub/article/view/957", "title": "A Review on Enterprise Data Lake Solutions", "version": 2, "registered_via": "crossref", "prefix": { "prefix": "10.46243", "status": "live" }, "registrant": { "name": "Longman Publishers", "kind": "publisher", "country": "India" }, "reserved_at": null, "registered_at": "2026-09-29 22:00:41", "updated_at": "2026-09-30 00:00:01", "withdrawn_at": null, "withdrawn_reason": null, "record": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2021.v6.i04.pp11-14", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "A Review on Enterprise Data Lake Solutions", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2021.v6.i04.pp11-14" } ], "agents": [ { "role": "author", "name": { "given": "Aakash", "family": "Aundhkar" }, "sequence": "first" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2021-08-16", "date_type": "PublicationDate", "online": "2021-08-16" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "06", "issue": "01", "pages": { "first": "11", "last": "14" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/957", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/957/873", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/957/4177", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://www.jst.org.in/index.php/pub/article/view/957/873", "purpose": "similarity-checking" } ], "abstract": { "value": "Data Lake is a highly flexible storage solution that can store both structured and unstructured data and operates on the schema-on-read approach. It acts as a potential alternative to the current Big Data storage issue. However, it does have certain flaws, such as inadequate authentication and access control. This paper examines a few of the current business Data Lake strategies. Apache Hadoop is generally regarded as the data lake industry standard. Its parallel processing systems ensure high-speed processing of massive volumes of data. Many businesses have attempted to build Hadoop wrappers in order to resolve questions about its raw state and lack of data protection. Platforms including Amazon Web Services (AWS) Data Lake and Azure Data Lake fall under this category. AWS Data Lake provides a more straightforward approach with failsafes to avoid data failure, while Azure Data Lake offers much greater scalability and enterprise-level reliability. Data Lake systems are becoming increasingly common in a variety of sectors, including finance, business intelligence, engineering, and healthcare.", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2021-08-16", "applies_to": "vor" }, "references": [ { "key": "ref1", "unstructured": "Tanmay Sanjay Hukkeri, VanshikaKanoria, Jyoti Shetty, A Study of Enterprise Data Lake Solutions, International Research Journal of Engineering and Technology (IRJET) Volume: 07 Issue: 05 | May 2020" }, { "key": "ref2", "unstructured": "SnezhanaSulova,The Usage of Data Lake For Business Intelligence Data Analysis, Conference Paper · October 2019" }, { "key": "ref3", "unstructured": "Surabhi DHegde, Ravinarayana B, Survey Paper on Data Lake, International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064" }, { "key": "ref4", "doi": "10.1109/access.2018.2858256", "unstructured": "Amra. Munshi, and Yasser Abdel-Rady I. Mohamed, Data Lake Lambda Architecture for Smart Grids Big Data Analytics, IEEE, date of publication July 23" }, { "key": "ref5", "doi": "10.1145/3035918.3056100", "unstructured": "Raghu Ramakrishna, Azure Data Lake Store: A Hyperscale Distributed File Service for Big Data Analytics" }, { "key": "ref6", "unstructured": "Ekta Maini, BonduVenkateswarlu, Data Lake-An Optimum Solution for Storage and Analytics of Big Data in Cardiovascular Disease Prediction System,IJCEM International Journal of Computational Engineering & Management, Vol. 21 Issue 6, November 2018" }, { "key": "ref7", "doi": "10.2139/ssrn.3490476", "unstructured": "DarkoGolec, Data Lake Architecture for a Banking Data Model, ENTRENOVA 12-14, 2019" }, { "key": "ref8", "doi": "10.1016/j.procs.2018.10.071", "unstructured": "Marilex Rea Llave, Data lakes in business intelligence: reporting from the trenches, Procedia Computer Science 138 (2018) 516–524" } ], "record": { "registrant": "Longman Publishers", "registered": "2026-09-15", "updated": "2026-09-20", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "record_sha256": "90c97686d83d4fd43b63b6e82483bdca5854acb72c5bc762eb29082262b13ef7", "handle": { "synced_at": "2026-10-01 18:14:01", "url": "https://www.jst.org.in/index.php/pub/article/view/957" }, "links": { "record_page": "https://registry.smartscholars.in/record.php?doi=10.46243%2Fjst.2021.v6.i04.pp11-14", "system_metadata": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2021.v6.i04.pp11-14&as=system", "history": "https://registry.smartscholars.in/api.php?action=history&doi=10.46243%2Fjst.2021.v6.i04.pp11-14", "kernel_xml": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2021.v6.i04.pp11-14&as=xml" } }