Advertisement

Near Real-Time Data Warehousing with Multi-stage Trickle and Flip

  • Janis Zuters
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 90)

Abstract

A data warehouse typically is a collection of historical data designed for decision support, so it is updated from the sources periodically, mostly on a daily basis. Today’s business however asks for fresher data. Real-time warehousing is one of the trends to accomplish this, but there are a number of challenges to move towards true real-time. This paper proposes ‘Multi-stage Trickle & flip’ methodology for data warehouse refreshment. It is based on the ‘Trickle & flip’ principle and extended in order to further insulate loading and querying activities, thus enabling both of them to be more efficient.

Keywords

Real-time data warehousing data refreshment data loading 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Jarke, M., Lenzerini, M., Vassiliou, Y.: Panos Vassiliadis. Fundamentals of Data Warehouses, 2nd, rev. and extended ed., XIV. Springer, Heidelberg (2003)Google Scholar
  2. 2.
    Inmon, W.H., Terdeman, R.H., Norris-Montanari, J., Meers, D.: Data Warehousing for E-Business. J. Wiley & Sons, Chichester (2001)Google Scholar
  3. 3.
    Santos, R.J., Bernardino, J.: Real-time Warehouse Loading Methodology. In: Proceedings of the 2008 International Symposium on Database Engineering & Applications (IDEAS 2008). ACM, New York (2008)Google Scholar
  4. 4.
    Langseth, J.: Real-time data warehousing: Challenges and solutions, DSSResources.COM (2004), http://dssresources.com/papers/features/langseth/langseth02082004.html
  5. 5.
    Jörg, T., Dessloch, S.: Near real-time data warehousing using state-of-the-art ETL tools. In: Castellanos, M., Dayal, U., Miller, R.J. (eds.) BIRTE 2009. LNBIP, vol. 41, pp. 100–117. Springer, Heidelberg (2010)Google Scholar
  6. 6.
    Kimball, R., Caserta, J.: The data warehouse ETL toolkit: Practical techniques for extracting, cleaning, conforming, and delivering data. John Wiley & Sons, Chichester (2004)Google Scholar
  7. 7.
    Kimball, R., Ross, M., Thornthwaite, W., Mundy, J., Becker, B.: The Kimball Group Reader: Relentlessly Practical Tools for Data Warehousing and Business Intelligence. John Wiley & Sons, Chichester (2010)Google Scholar
  8. 8.
    Solodovnikova, D.: Building Queries on Multiple Versions of Data Warehouse. In: Proceedings of the Eighth International Baltic Conference (DB&IS 2008), Tallinn, Estonia, pp. 75–86 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Janis Zuters
    • 1
  1. 1.University of LatviaRigaLatvia

Personalised recommendations