Data Transformation Pipelines

Data Transformation Pipelines is a methodological framework within data engineering that orchestrates the systematic processing, cleaning, enrichment, and structuring of raw data into curated, analytics-ready formats for downstream consumption in a Data Lakehouse Architecture.

179

Publications

11K

Citations

675

Authors

320

Institutions

Publications per year

2017–2026

103

Authors

675

  1. GP
    Gilberto Pastorello

    Lawrence Berkeley National Laboratory, United States

    3 publications · 48 citations

    3

    h-index

  2. IF
    Ihab F. Ilyas

    University of Waterloo, Canada

    3 publications · 77 citations

    3

    h-index

  3. ZJ
    Zhongjun Jin

    University of Michigan, United States

    3 publications · 152 citations

    3

    h-index

  4. MM
    Martin Morgan

    Washington State University, United States

    2 publications · 979 citations

    2

    h-index

  5. ZZ
    Zijian Zheng

    Microsoft (United States), United States

    2 publications · 120 citations

    2

    h-index

1–5 of 675

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 7 publications · 175 citations

    7

    h-index

  2. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 6 publications · 273 citations

    6

    h-index

  3. Bioinformatics

    5 publications · 2.4K citations

    5

    h-index

  4. arXiv (Cornell University)

    Repository · 4 publications · 69 citations

    4

    h-index

  5. Expert Systems with Applications

    4 publications · 114 citations

    4

    h-index

1–5