Concepedia

Data Lake Benchmarking

Data Lake Benchmarking is a systematic evaluation framework that distinguishes itself by quantifying the performance, scalability, and functionality of data lake architectures through standardized workloads, metrics, and comparative analysis against established baselines.

1

Publications

33

Citations

23

Authors

1

Institutions

Publications per year

2017–2026

1

Parents

Publications

1

1–2 of 1

Authors

23

  1. TG
    Todd Greenstein

    Databricks (United States)

    1 publications · 33 citations

    1

    h-index

  2. SP
    Sameer Paranjpye

    Databricks (United States)

    1 publications · 33 citations

    1

    h-index

  3. TV
    Tom van Bussel

    Databricks (United States)

    1 publications · 33 citations

    1

    h-index

  4. AL
    Ala Luszczak

    Databricks (United States)

    1 publications · 33 citations

    1

    h-index

  5. BS
    Bart Samwel

    Google (United States), United States

    1 publications · 33 citations

    1

    h-index

1–5 of 23

Institutions

1

  1. Databricks (United States)

    San Francisco, United States

    1 h-index

    17 publications

    561 citations

  2. University of Arizona

    Tucson, United States

    1 publications

    19 citations

  3. Beijing Institute of Technology

    Beijing, China

    1 publications

    19 citations

1–3 of 1

Venues

  1. 1

    h-index

  2. Proceedings of the VLDB Endowment

    PVLDB · 1 publications · 19 citations

1–2