Trends and Challenges in Big Data Processing
Summary: Reflects on Spark’s success as a unified in-memory engine spanning batch, SQL, streaming, and iterative workloads. Identifies challenges for next-generation systems as memory scaling slows and GPUs, FPGAs, and 3D XPoint reshape hardware. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Ion Stoica (University of California Berkeley)
BibTeX Citation
@article{stoica_vldb16,
title = {{Trends and Challenges in Big Data Processing}},
author = {Stoica, Ion},
journal = {PVLDB},
series = {{VLDB} '16},
volume = {9},
number = {13},
pages = {1619},
doi = {10.14778/3007263.3007324},
url = {https://doi.org/10.14778/3007263.3007324},
year = {2016}
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