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Databricks launches LTAP (Lake Transactional/Analytical Processing), a new architecture that unifies OLAP and OLTP on a single copy of data in the lake, eliminating ETL pipelines and powered by Lakebase. This provides a single governed foundation for operational, analytical, and streaming data in the AI application era.
This paper presents SANA, a diagnostic ablation framework for exploratory question answering (EQA) over data lakes, which decomposes end-to-end agent failures into search, planning, data analysis, and policy components. Evaluations on LakeQA and KramaBench reveal data analysis as a consistent bottleneck, with search being a major limitation in large-scale settings.
LakeQA is a new benchmark for exploratory question answering over a million-scale data lake, evaluating multi-hop reasoning and compositionality across text, tables, and knowledge graphs.