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Data lake enables hybrid product search, cutting catalog copies

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to enable hybrid product search directly from data lake tables, eliminating the need for separate copies of e-commerce catalogs. This approach leverages Apache Pinot, an OLAP engine, to query data lakehouse tables like Apache Iceberg over Apache Parquet files. The system integrates BM25 lexical scoring with vector search, serving results directly from the data lake with minimal latency after updates, significantly reducing storage and operational overhead. AI

IMPACT Reduces infrastructure costs and complexity for e-commerce search, potentially enabling faster updates and more efficient operations.

RANK_REASON Academic paper detailing a new technical approach to data retrieval. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Data lake enables hybrid product search, cutting catalog copies

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Academic paper detailing a new technical approach to data retrieval. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Chinmay Soman ·

    Hybrid Product Search Without the Copy: Lexical, Semantic and Filtered Retrieval Served Directly from a Data Lake

    E-commerce catalogs increasingly live in lakehouse tables: Apache Iceberg metadata over Parquet files in object storage. Product search, however, still runs on copies of that catalog, in a lexical engine and a vector database, each with its own ingestion pipeline, schema, stalene…

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