10.46243/jst.2021.v6.i04.pp11-14 registered
A Review on Enterprise Data Lake Solutions
Resolves to https://www.jst.org.in/index.php/pub/article/view/957
Held by Longman Publishers (India) · prefix 10.46243 live · DOI address https://doi.org/10.46243/jst.2021.v6.i04.pp11-14
Registered 29 Sep 2026 via crossref · record version 2 · last change 30 Sep 2026, 12:00 AM · record sha256 90c97686d83d4fd4…
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What the DOI identifies
JournalArticle — an article in a journal · Digital · Visual · en
A Review on Enterprise Data Lake Solutions (PrincipalTitle)
Published 2021-08-16
Part of Journal of Science & Technology · ISSN 2456-5660 · volume 06 · issue 01 · pages 11–14
Agents
- Aakash Aundhkar (author)
- Longman Publishers (publisher)
Identifiers DOI 10.46243/jst.2021.v6.i04.pp11-14
Abstract
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.
System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1
Each element by the standard's name (Annex B: reference elements, then administrative) and the Handbook's (in grey), read off the record above.
| Element | Value | In the record |
|---|---|---|
| DOI Name DOI name | 10.46243/jst.2021.v6.i04.pp11-14 | doi |
| Referent Type referentType | Creation | referent |
| Referent Sub-Type referentSubType | JournalArticle — an article in a journal | type |
| Referent Name(s) referentName(s) | A Review on Enterprise Data Lake Solutions (PrincipalTitle, en) | titles |
| Basic Metadata basicMetadata | author: Aakash Aundhkar publisher: Longman Publishers published: 2021-08-16 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 06 · no. 01 · pp. 11–14 language: en form: Digital · Visual · Language | agents, dates, container, language, structural_type, modes, characters |
| Referent Identifier(s) alternateIdentifier(s) | none besides the DOI | identifiers, relations (IsSameAs) |
| Registration Authority registrationAuthorityCode | Crossref — issued by Crossref (member 25296); held here as a copy | record.source_agency (our code, ra_doi_name, for names issued here once appointed) |
| Created Date issueDate | 2026-09-15 | record.registered (when the DOI name was first registered) |
| relatedIdentifiers | none needed — the descriptive metadata is in this record | container, relations (only where the descriptive metadata lives at another identifier) |
complete Every System Metadata element is here, with the basic metadata a journal article needs.
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History — the ledger
Every change to this DOI, in order, as it was recorded. Entries are only ever added, never changed or removed.
| # | When | What | By | Changes |
|---|---|---|---|---|
| 1 | 29 Sep 2026, 10:00 PM | register registered at Crossref; record read from api.crossref.org | Administrator (admin) | 74 fields set · sha256 897c7a728982… |
| 2 | 30 Sep 2026, 12:00 AM | update record re-read from api.crossref.org | Administrator (admin) | container.titles.0.value: |
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