Skip to main content
SpringerLink
Log in

Data Architecture and Design

  • Living reference work entry
  • First Online:
Encyclopedia of Big Data

  • 136 Accesses

Introduction

The availability of Big Data sets has led many organizations to shift their emphases from supporting transaction-oriented data processing to supporting data-centric analytics and applications. The increasing rapidity of dynamic data flows, such as those generated by IoT applications and devices, the increasing sophistication of interoperability mechanisms, and the concomitant decreasing costs of data storage have transformed not only data acquisition and management paradigms but have also overloaded available ICT resources, thereby diminishing their capabilities to support organizational data and information requirements. Due to the difficulties of managing Big Data sets and increasingly more complex analytical models, transaction processing-focused ICT architectures that were sufficient to manage small data sets may require enhancements and re-purposing to support Big Data analytics.

A number of properties inform properly designed Big Data system architectures. Such...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Further Readings

  • NIST Big Data Public Working Group Reference Architecture Subgroup. (2015). NIST big data interoperability framework: volume 6: Reference architecture. Washington DC: US Department of Commerce National Institute of Standards and Technology. Downloaded from https://bigdatawg.nist.gov.

  • Santos, M. Y., Sá, J., Costa, C., Galvão, J., Andrade, C., Martinho, B., Lima, F. V., & Costa, E. (2017). A big data analytics architecture for industry 4.0. In A. Rocha, A. Correia, H. Adeli, L. Reis, & S. Costanzo (Eds.), Recent advances in information systems and technologies. WorldCIST 2017 (Advances in intelligent systems and computing) (Vol. 570, pp. 175–184). Cham: Springer. (Porto Santo Island, Madeira, Portugal).

    Google Scholar 

  • Viana, P., & Sato, L. (2015). A proposal for a reference architecture for long-term archiving, preservation, and retrieval of big data. In 13th international conference on Trust, Security and Privacy in Computing and Communications, (TrustCom) (pp. 622–629). Beijing: IEEE Computer Society.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. George Mason University, Arlington, VA, USA

    Erik W. Kuiler

Authors
  1. Erik W. Kuiler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erik W. Kuiler .

Editor information

Editors and Affiliations

  1. School of Policy, Government and International Affairs, George Mason University, Fairfax, VA, USA

    Laurie A. Schintler

  2. School of Policy, Government and International Affairs, George Mason University, Fairfax, VA, USA

    Connie L. McNeely

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Kuiler, E.W. (2019). Data Architecture and Design. In: Schintler, L., McNeely, C. (eds) Encyclopedia of Big Data. Springer, Cham. https://doi.org/10.1007/978-3-319-32001-4_297-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-32001-4_297-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32001-4

  • Online ISBN: 978-3-319-32001-4

  • eBook Packages: Springer Reference Business and ManagementReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

Publish with us

Policies and ethics

Search

Navigation