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Related Experiment Video

Updated: Jun 25, 2026

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

Democratizing cloud data lake analytics: natural language access to Apache Iceberg via LLM agents.

Vipin Kataria1, Nitin Kumar2

  • 1Picarro Inc., Santa Clara, CA, United States.

Frontiers in Big Data
|June 24, 2026
PubMed
Summary

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The LangChain Iceberg Toolkit allows non-technical users to query enterprise data lakes using natural language. This system enhances data access security and accuracy, outperforming existing text-to-SQL methods.

Area of Science:

  • Data Engineering
  • Artificial Intelligence
  • Database Systems

Background:

  • Enterprise data lakes are crucial for business insights but inaccessible to non-technical users due to SQL requirements.
  • Existing text-to-SQL models suffer from security vulnerabilities (SQL injection) and limitations in handling data lake specifics.
  • Inconsistent metric definitions across organizations hinder reliable data analysis.

Purpose of the Study:

  • To develop a natural language interface for querying Apache Iceberg data lakes.
  • To address security vulnerabilities and improve the accuracy of text-to-SQL systems.
  • To enable business analysts to access data insights without SQL expertise or schema knowledge.

Main Methods:

  • Implemented the LangChain Iceberg Toolkit utilizing large language models (LLMs) for natural language understanding.
Keywords:
Apache IcebergLangChainSQL-free queryingconversational data accessdata lakeslarge language modelsnatural language processingsemantic layer

Related Experiment Videos

Last Updated: Jun 25, 2026

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

  • Employed a hybrid query execution architecture combining PyIceberg's type-safe API and DuckDB's SQL engine.
  • Integrated a YAML-based semantic layer for mapping business terms to data structures and ensuring consistent metric definitions.

    • Main Results:

    • Achieved 100% query success rate across 100 systematically designed queries.
    • Demonstrated a 33 percentage-point accuracy improvement over a schema-aware text-to-SQL baseline (100% vs. 67%).
    • Reduced SQL injection attack success rate to 0%, a significant improvement from prior research.

    Conclusions:

    • The LangChain Iceberg Toolkit democratizes enterprise data access by enabling natural language querying of Apache Iceberg data lakes.
    • The system offers a secure, accurate, and efficient solution for data insights, overcoming limitations of previous text-to-SQL approaches.
    • The production-ready, open-source implementation validates the practical viability of conversational data access.