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Liquid: Unifying Nearline and Offline Big Data Integration

Summary: Liquid replaces DFS-centric batch stacks with a unified nearline+offline integration architecture: a stateful stream-processing layer for incremental, low-latency transforms plus a highly-available pub/sub messaging layer. Deployed at LinkedIn, it provides cost-efficient, scalable pre-processing for real-time and batch consumers. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
267
Venue
CIDR
Year
2015
Pagerank
5.7879923e-05
Overall Rank
6,734 | 53.80%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{fernandez_cidr15,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '15},
        title = {{Liquid: Unifying Nearline and Offline Big Data Integration}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Fernandez, Raul Castro and Pietzuch, Peter and Kreps, Jay and Narkhede, Neha and Rao, Jun and Koshy, Joel and Lin, Dong and Riccomini, Chris and Wang, Guozhang},
        year = {2015}
}

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