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We demonstrate the first fully open source text-to-analytics stack for Apache Iceberg data in a simple workflow. This stack brings together three open source technologies: PyDough, BodoSQL, and Apache Iceberg, an open table format for managing large datasets. Together, they provide the foundation fo
We introduce the latest version of BodoSQL, designed for high-performance interactive query execution on massive datasets. Available in the BodoSQL 2026.6 release, this update introduces a new C++-based execution backend that dramatically reduces query startup latency while preserving the scalabilit
Here is a deep look into how we designed our GPU-native Iceberg source operator, the architectural tradeoffs we weighed, and the specific edges we encountered while making the storage layer safe for hardware acceleration.
Here is a look at the internals of how we built our native GPU Iceberg writer. By building Iceberg write support directly into Bodo’s native execution engine, the result is not simply a faster Parquet write path—but a GPU-native Iceberg sink that preserves the throughput of the broader distributed p
Benchmarking all 22 TPC-H queries on a multi-node GPU cluster and show an overall speedup of more than 3× over Dask-CuDF across the full benchmark suite. These results highlight the advantages of an MPI-based SPMD execution model for communication-intensive analytical workloads.
By combining a database-grade optimizer, streaming execution, and an efficient HPC-style distributed runtime, Bodo optimizes and executes entire workloads across nodes and devices, maximizing performance while minimizing overhead.
In PyDough, accuracy does not depend primarily on prompt tuning or semantic-layer completeness. For relational reasoning to be reliable—especially in the context of large language models—it must sit on top of an explicit structural model of meaning. In PyDough, that model is a knowledge graph.
Pandas 3 takes a major step forward in performance and scalability by introducing native Bodo JIT integration for accelerating UDFs, and native Apache Iceberg support for reliable, scalable data management. We walk through both integrations and show how to get started.
We compare Bodo DataFrames, Dask, and PySpark on a cluster of four Amazon EC2 instances (128 physical cores).
As analytics becomes more conversational, iterative, and collaborative, that gap matters more than ever. PyDough is our attempt to close it—not by generating better SQL, but by giving people a language that’s closer to how they actually think about their data. In this post, we walk through the speci
The PyIceberg 0.10 release introduces a new first-class integration: Table.to_bodo(). With this method, PyIceberg now plugs directly into Bodo’s high performance distributed DataFrame engine, all without leaving the Pandas API.