Data debugging and exploration with vizier

Mike Brachmann, Carlos Bautista, Sonia Castelo, Su Feng, Juliana Freire, Boris Glavic, Oliver Kennedy, Heiko Müller, Rémi Rampin, William Spoth, Ying Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present Vizier, a multi-modal data exploration and debugging tool. The system supports a wide range of operations by seamlessly integrating Python, SQL, and automated data curation and debugging methods. Using Spark as an execution backend, Vizier handles large datasets in multiple formats. Ease-of-use is attained through integration of a notebook with a spreadsheet-style interface and with visualizations that guide and support the user in the loop. In addition, native support for provenance and versioning enable collaboration and uncertainty management. In this demonstration we will illustrate the diverse features of the system using several realistic data science tasks based on real data.

Original languageEnglish (US)
Title of host publicationSIGMOD 2019 - Proceedings of the 2019 International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages1877-1880
Number of pages4
ISBN (Electronic)9781450356435
DOIs
StatePublished - Jun 25 2019
Event2019 International Conference on Management of Data, SIGMOD 2019 - Amsterdam, Netherlands
Duration: Jun 30 2019Jul 5 2019

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Conference

Conference2019 International Conference on Management of Data, SIGMOD 2019
CountryNetherlands
CityAmsterdam
Period6/30/197/5/19

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Brachmann, M., Bautista, C., Castelo, S., Feng, S., Freire, J., Glavic, B., Kennedy, O., Müller, H., Rampin, R., Spoth, W., & Yang, Y. (2019). Data debugging and exploration with vizier. In SIGMOD 2019 - Proceedings of the 2019 International Conference on Management of Data (pp. 1877-1880). (Proceedings of the ACM SIGMOD International Conference on Management of Data). Association for Computing Machinery. https://doi.org/10.1145/3299869.3320246