Bodo gives teams a modern foundation for analytics in the AI era — where business users, data teams, and agents can work in the languages and tools they already know, with the trust and performance their enterprises require.
Open and modular
Built on open source foundations under the Apache 2.0 license.
Works with your stack
Keep the languages and workflows your teams already know.
Built-in governance
Keep analytics workflows governed, validated, and under enterprise control.
Performance at scale
Accelerate analytics and AI workflows from question to execution.
Open and modular
Built on open source foundations under the Apache 2.0 license.
Works with your stack
Keep the languages and workflows your teams already know.
Built-in governance
Keep analytics workflows governed, validated, and under enterprise control.
Performance at scale
Accelerate analytics and AI workflows from question to execution.
The most accurate, secure, and verifiable answer layer for enterprise AI—enabling anyone to safely turn their enterprise data into trusted answers.
Bodo Engine gives data teams the performance of a distributed compute engine with the simplicity of the tools they already know. Run Pandas, Python, and SQL across CPUs and GPUs — with no data migration, no workflow changes, and no code changes.
Faster than Polars multi-GPU
Faster 3× than Dask-CuDF
The latest and greatest benchmarks, comparisons, use cases, and other product news from the Bodo engineering team.
How PyDough Reached 100% Accuracy on dbt’s Semantic Layer Benchmark
dbt Labs recently published an 11-question benchmark comparing its Semantic Layer with direct text-to-SQL. We ran the same questions through our PyDough-based pipeline. Across 20 independent runs per question, our pipeline answered 220 out of 220 correctly: 100% accuracy.
How BodoSQL Uses and Extends Apache Calcite: From SQL Text to Optimized Execution Plans
We explore how BodoSQL uses Calcite, explain the role of each major component and how we customized it for our use case, and finally discuss how we keep those customizations up-to-date as the upstream project evolves.
PyDough vs. Malloy vs. PRQL: What's the Difference?
We’ll walk through how each system: represents relationships between datasets, constructs queries from that representation, and constrains (or fails to constrain) the space of possible queries.
Have questions or want a personalized demo? Just give our team a shout—we’d love to chat!
