IDEAS home Printed from https://ideas.repec.org/a/aes/infoec/v20y2016i1p48-61.html
   My bibliography  Save this article

Data Processing Languages for Business Intelligence. SQL vs. R

Author

Listed:
  • Marin FOTACHE

Abstract

As data centric approach, Business Intelligence (BI) deals with the storage, integration, processing, exploration and analysis of information gathered from multiple sources in various formats and volumes. BI systems are generally synonymous to costly, complex platforms that require vast organizational resources. But there is also an-other face of BI, that of a pool of data sources, applications, services developed at different times using different technologies. This is “democratic†BI or, in some cases, “fragmented†, “patched†(or “chaotic†) BI. Fragmentation creates not only integration problems, but also supports BI agility as new modules can be quickly developed. Among various languages and tools that cover large extents of BI activities, SQL and R are instrumental for both BI platform developers and BI users. SQL and R address both monolithic and democratic BI. This paper compares essential data processing features of two languages, identifying similarities and differences among them and also their strengths and limits.

Suggested Citation

  • Marin FOTACHE, 2016. "Data Processing Languages for Business Intelligence. SQL vs. R," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 20(1), pages 48-61.
    • Handle: RePEc:aes:infoec:v:20:y:2016:i:1:p:48-61
    as

    Download full text from publisher

    File URL: http://revistaie.ase.ro/content/77/05%20-%20Fotache.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kane, Michael & Emerson, John W. & Weston, Stephen, 2013. "Scalable Strategies for Computing with Massive Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i14).
    2. Octavian DOSPINESCU & Marian PERCA, 2013. "Web Services in Mobile Applications," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 17(2), pages 17-26.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sabina-Cristiana Necula & Cătălin Strîmbei, 2019. "People Analytics of Semantic Web Human Resource Résumés for Sustainable Talent Acquisition," Sustainability, MDPI, vol. 11(13), pages 1-18, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Junyang Qian & Yosuke Tanigawa & Wenfei Du & Matthew Aguirre & Chris Chang & Robert Tibshirani & Manuel A Rivas & Trevor Hastie, 2020. "A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank," PLOS Genetics, Public Library of Science, vol. 16(10), pages 1-30, October.
    2. Bandara, Kanchana & Varpe, Øystein & Ji, Rubao & Eiane, Ketil, 2018. "A high-resolution modeling study on diel and seasonal vertical migrations of high-latitude copepods," Ecological Modelling, Elsevier, vol. 368(C), pages 357-376.
    3. Aaron T L Lun & Hervé Pagès & Mike L Smith, 2018. "beachmat: A Bioconductor C++ API for accessing high-throughput biological data from a variety of R matrix types," PLOS Computational Biology, Public Library of Science, vol. 14(5), pages 1-15, May.
    4. Julian VASILEV, 2014. "Web Service for Calculating the Probability of Returning a Loan – De-sign, Implementation and Deployment," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 18(4), pages 111-119.
    5. Fulya Gokalp Yavuz & Barret Schloerke, 2020. "Parallel computing in linear mixed models," Computational Statistics, Springer, vol. 35(3), pages 1273-1289, September.
    6. Fang, Jianglin, 2023. "A split-and-conquer variable selection approach for high-dimensional general semiparametric models with massive data," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
    7. Mariusz Nowak & Piotr Derbis & Krzysztof Kurowski & Rafał Różycki & Grzegorz Waligóra, 2021. "LPWAN Networks for Energy Meters Reading and Monitoring Power Supply Network in Intelligent Buildings," Energies, MDPI, vol. 14(23), pages 1-14, November.
    8. Hofert, Marius & Mächler, Martin, 2016. "Parallel and Other Simulations in R Made Easy: An End-to-End Study," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i04).
    9. Lining Yu & Wolfgang Karl Hardle & Lukas Borke & Thijs Benschop, 2020. "An AI approach to measuring financial risk," Papers 2009.13222, arXiv.org.
    10. Lining Yu & Wolfgang Karl Härdle & Lukas Borke & Thijs Benschop, 2017. "FRM: a Financial Risk Meter based on penalizing tail events occurrence," SFB 649 Discussion Papers SFB649DP2017-003, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    11. Bischl, Bernd & Lang, Michel & Mersmann, Olaf & Rahnenführer, Jörg & Weihs, Claus, 2015. "BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i11).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aes:infoec:v:20:y:2016:i:1:p:48-61. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Paul Pocatilu (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.