Collaborative data analytics with datahub

Anant Bhardwaj, Amol Deshpande, Aaron J. Elmore, David Karger, Sam Madden, Aditya Parameswaran, Harihar Subramanyam, Eugene Wu, Rebecca Zhang

Research output: Chapter in Book/Report/Conference proceedingChapter


While there have been many solutions proposed for storing and analyzing large volumes of data, all of these solutions have limited support for collaborative data analytics, especially given the many individuals and teams are simultaneously analyzing, modifying and exchanging datasets, employing a number of heterogeneous tools or languages for data analysis, and writing scripts to clean, preprocess, or query data. We demonstrate DataHub, a unified platform with the ability to load, store, query, collaboratively analyze, interactively visualize, interface with external applications, and share datasets. We will demonstrate the following aspects of the DataHub platform: (a) flexible data storage, sharing, and native versioning capabilities: multiple conference attendees can concurrently update the database and browse the different versions and inspect conflicts; (b) an app ecosystem that hosts apps for various dataprocessing activities: conference attendees will be able to effortlessly ingest, query, and visualize data using our existing apps; (c) thrift-based data serialization permits data analysis in any combination of 20+ languages, with DataHub as the common data store: conference attendees will be able to analyze datasets in R, Python, and Matlab, while the inputs and the results are still stored in DataHub. In particular, conference attendees will be able to use the DataHub notebook-an IPython-based notebook for analyzing data and storing the results of data analysis.

Original languageEnglish (US)
Title of host publicationProceedings of the VLDB Endowment
PublisherAssociation for Computing Machinery
Number of pages4
StatePublished - Jan 1 2015
Event3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006 - Seoul, Korea, Republic of
Duration: Sep 11 2006Sep 11 2006

Publication series

NameProceedings of the VLDB Endowment
ISSN (Electronic)2150-8097


Other3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006
CountryKorea, Republic of

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

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  • Cite this

    Bhardwaj, A., Deshpande, A., Elmore, A. J., Karger, D., Madden, S., Parameswaran, A., Subramanyam, H., Wu, E., & Zhang, R. (2015). Collaborative data analytics with datahub. In Proceedings of the VLDB Endowment (12 ed., pp. 1916-1919). (Proceedings of the VLDB Endowment; Vol. 8, No. 12). Association for Computing Machinery.