Ant Group has developed OmniTable, a unified wide-table system designed to streamline the data preparation process for large language models. This system, which earned the Best Paper Award in the Industrial Track at VLDB 2026, manages over 35 PB of training data, significantly improving efficiency. OmniTable reorganizes data by presenting it as a single logical wide table, with features treated as system assets, thereby reducing the complexity of data positioning, feature re-computation, and result traceability. The system also isolates errors at the record level, preventing minor data anomalies from halting entire processing batches and optimizing resource utilization through techniques like operator fusion. AI
IMPACT Streamlines large-scale data preparation for LLMs, potentially accelerating model development cycles and reducing infrastructure costs.
RANK_REASON Research paper award for a data system. [lever_c_demoted from research: ic=1 ai=0.7]
- Ant Group
- code
- International Conference on Very Large Data Bases
- OmniTable
- QbitAI
- supervised fine-tuning
- VLDB 2026
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