Cite this DOI

Aundhkar, A. (2021). A Review on Enterprise Data Lake Solutions. *Journal of Science & Technology*, *06*(01), 11–14. https://doi.org/10.46243/jst.2021.v6.i04.pp11-14

@article{aundhkar2021review,
  author    = {Aundhkar, Aakash},
  title     = {{A Review on Enterprise Data Lake Solutions}},
  journal   = {Journal of Science \& Technology},
  year      = {2021},
  month     = {aug},
  volume    = {06},
  number    = {01},
  pages     = {11--14},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2021.v6.i04.pp11-14},
  url       = {https://doi.org/10.46243/jst.2021.v6.i04.pp11-14},
  language  = {en},
  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.}
}

TY  - JOUR
TI  - A Review on Enterprise Data Lake Solutions
AU  - Aundhkar, Aakash
JO  - Journal of Science & Technology
PY  - 2021
DA  - 2021/08/16/
VL  - 06
IS  - 01
SP  - 11
EP  - 14
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - 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.
DO  - 10.46243/jst.2021.v6.i04.pp11-14
UR  - https://doi.org/10.46243/jst.2021.v6.i04.pp11-14
ER  -

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    "URL": "https://doi.org/10.46243/jst.2021.v6.i04.pp11-14",
    "title": "A Review on Enterprise Data Lake Solutions",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
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    "publisher": "Longman Publishers",
    "language": "en",
    "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.",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2021.v6.i04.pp11-14 gives all four in one JSON answer.