Fully Open Source Trustworthy Text-to-Analytics Stack for Iceberg Data with PyDough and BodoSQL

An open-source text-to-analytics stack for Apache Iceberg data is demonstrated, integrating PyDough, BodoSQL, and Apache Iceberg to create a workflow that is both user-friendly and scalable. This architecture addresses the challenges of trustworthiness and scalability in production analytics by translating natural language queries into reliable SQL executions. PyDough acts as a semantic layer that converts natural language into analytical logic, while BodoSQL serves as the distributed query engine executing queries with low latency on Iceberg tables. The workflow begins by setting up an Iceberg database from a synthetic dataset, converting it into Iceberg tables, and establishing a BodoSQL context. Metadata is generated through a PyDough knowledge graph that aids in converting code to SQL, ensuring semantic validation of queries. The stack enables interaction with large datasets by allowing users to ask questions in natural language, which are interpreted by an LLM, translated into PyDough code, and then executed efficiently by the BodoSQL engine. The system optimizes query execution using techniques such as filter pushdown and dynamic join ordering, ensuring that analytics remain interactive and performant. This comprehensive approach combines natural language processing with robust data management and query execution, offering a scalable solution for analytics over large datasets.

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