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Data Wrangling: The Challenging Journey from the Wild to the Lake

Summary: Characterizes data-wrangling pain points for data lakes—difficulties in acquisition, interpretation, description, maintenance, provenance, governance and scaling as sources multiply. Advocates shifting from “raw” lakes to curated data lakes via systematic curation, metadata, quality and governance pipelines to enable truly usable ad‑hoc analytics beyond enterprise IT. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
262
Venue
CIDR
Year
2015
Pagerank
0.00010378976
Overall Rank
1,833 | 87.25%
DOI
-

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