New Data Warehouse Technologies

  • Alejandro Vaisman
  • Esteban Zimányi
Part of the Data-Centric Systems and Applications book series (DCSA)


Big data refers to large collections of data that may be unstructured or may grow so large and at such a high pace that it is difficult to manage them with standard database systems or analysis tools. Examples of big data include web logs, radio-frequency identification tags, sensor networks, and social networks, among other ones. It has been reported as of the time of writing this book that 7 and 10 terabytes of data are added and processed, respectively, by Twitter and Facebook every day. Approximately 80% of these data are unstructured, and 90% of them have been created in the last 2 years. Management and analysis of these massive amounts of data demand new solutions that go beyond the traditional processes or software tools. All of these have great implications on the way data warehousing practice is going to be performed in the future. For instance, big data analytics requires in many cases the data latency (the time elapsed between the moment some data are collected and the action based on such data is taken) to be dramatically reduced. Thus, near real-time data management techniques must be developed. Also, external data sources like the semantic web may need to be queried.


Main Memory Data Warehouse Business Intelligence Fact Table Query Processor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Alejandro Vaisman
    • 1
  • Esteban Zimányi
    • 2
  1. 1.Instituto Tecnológico de Buenos AiresBuenos AiresArgentina
  2. 2.Université Libre de BruxellesBrusselsBelgium

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