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Emerging trends in the enterprise data analytics: connecting Hadoop and DB2 warehouse

Published: 12 June 2011 Publication History

Abstract

Enterprises are dealing with ever increasing volumes of data, reaching into the petabyte scale. With many of our customer engagements, we are observing an emerging trend: They are using Hadoop-based solutions in conjunction with their data warehouses. They are using Hadoop to deal with the data volume, as well as the lack of strict structure in their data to conduct various analyses, including but not limited to Web log analysis, sophisticated data mining, machine learning and model building. This first stage of the analysis is off-line and suitable for Hadoop. But, once their data is summarized or cleansed enough, and their models are built, they are loading the results into a warehouse for interactive querying and report generation. At this later stage, they leverage the wealth of business intelligence tools, which they are accustomed to, that exist for warehouses. In this paper, we outline this use case and discuss the bidirectional connectors we developed between IBM DB2 and IBM InfoSphere BigInsights.

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    cover image ACM Conferences
    SIGMOD '11: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
    June 2011
    1364 pages
    ISBN:9781450306614
    DOI:10.1145/1989323
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 12 June 2011

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    Author Tags

    1. DB2
    2. Hadoop
    3. federated database systems
    4. map/reduce
    5. system integration

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