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
179
Optimally estimating the sample standard deviation from the five‐number summary
Jiandong Shi, Dehui Luo, Hong Weng et al. · Research Synthesis Methods · 2020 · 576 citations
1–5 of 179
675
Lawrence Berkeley National Laboratory, United States
3 publications · 48 citations
3
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3
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3
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2
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2
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1–5 of 675
320

University of Waterloo
Waterloo, Canada
5 h-index
8 publications
222 citations
National University of Singapore
Singapore, Singapore
5 h-index
7 publications
320 citations
Massachusetts Institute of Technology
Cambridge, United States
5 h-index
7 publications
247 citations
University of Michigan
Ann Arbor, United States
4 h-index
10 publications
554 citations
Fred Hutch Cancer Center
Seattle, United States
3 h-index
13 publications
6.1K citations
King's College London
London, United Kingdom
3 h-index
5 publications
185 citations
University of Illinois Urbana-Champaign
Urbana, United States
3 h-index
13 publications
945 citations

Chinese Academy of Sciences
Beijing, China
3 h-index
4 publications
87 citations
1–8 of 320
PVLDB · 7 publications · 175 citations
7
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TVCG · 6 publications · 273 citations
6
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5 publications · 2.4K citations
5
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Repository · 4 publications · 69 citations
4
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4 publications · 114 citations
4
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1–5