Bodo Blog

Bodo Native Integration in PyIceberg 0.10: Bringing Scalability to PyIceberg with Pandas APIs

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.

From Iceberg to LoRa: A Unified LLM Fine-Tuning Pipeline with Bodo

We show how to use Bodo DataFrames to load and preprocess data directly from an Apache Iceberg table and feed it seamlessly into Bodo AI Toolkit to fine-tune a Llama 3.1 8B model using LoRa. We'll be training a chatbot on its own "liked" feedback to improve its responses.

PyTorch Conference 2025: Recap and Key Takeaways

Last week, some of us had the chance to attend PyTorch Conference 2025. We wanted to share some of our key takeaways from the conference; from distributed training to efficient inference, as well as highlight one aspect that is absolutely crucial but received less attention: data infrastructure.

Introducing PyDough-CE: A simpler, safer path for natural language analytics

Today we’re releasing PyDough Community Edition (PyDough-CE), an open source project that finally makes natural language analytics both practical and safe.

Using LangChain Agents + Bodo DataFrames on Large Datasets

We’ll show how to use langchain_bodo—Bodo’s integration with LangChain, a popular framework that lets you easily build AI agents—as well as the NYC Taxi dataset to create an agent capable of answering complex questions on real world data, such as estimating the average fare between Newark airport and Manhattan.

Python DataFrames (Bodo, Daft, Polars, PySpark, Dask, Modin/Ray) Compete for Your NYC Taxi Fare

In this post, we run the full NYC Taxi workload and compare Bodo DataFrames with both the earlier systems and the Bodo JIT compiler. 

Using LLMs at Scale: A Pandas-Native Approach

Scaling Amazon S3 Vectors Workflows Effortlessly in Python with Bodo

Iceberg I/O performance comparison at scale (Bodo vs PyIceberg, Spark, Daft)

Bodo 2025.7: Turbo‑Charging DataFrames for Production Lakes

Bodo’s July release updates the best of the Bodo DataFrame library and adds new features—from the best way to interact with Iceberg, to making database-grade analytics now possible!

Iceberg I/O Optimizations in Compute Engines – Summit Recap

At this year’s Iceberg Summit, I had the opportunity to present a talk alongside my colleague Srinivas Lade on I/O optimizations in compute engines, particularly within the context of the Bodo engine.

Bodo 2025.6 is Here: Supercharged Data Operations with Iceberg, Merge, and Sort

We are thrilled to announce the release of Bodo 2025.6, a major update focused on dramatically expanding the power and compatibility of our DataFrame library. This release brings some of the most requested and powerful data manipulation features to the Bodo dataframe libraryplatform, enabling you to do more with your data, faster than ever.