Bodo Blog

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.

Bodo Dataframe Library: First Look

Rethinking DataFrames: Easy as Pandas, Fast as a Data Warehouse

DataFrames have become the go-to abstraction for working with structured data in Python—and for good reason. Lightweight, intuitive, and versatile, libraries like Pandas have empowered millions of developers to explore and transform data easily. But as the scale and complexity of workloads have grown, a new question has emerged...

Key Takeaways from Iceberg Summit 2025

A few of us from Bodo recently attended the Iceberg Summit, and it was great to see how Iceberg is rapidly evolving to meet the demands of modern data infrastructure. As long-time champions of open table formats and scalable analytics, we were excited not just to attend—but also to contribute...

Data Processing Engines Comparison Pt 2.

We will use the same workload and settings from the previous analysis and provide new implementations in both Daft and Polars. To recap, this workload summarizes monthly taxi and rideshare trips from New York City’s Taxi and Limousine Commission (TLC) and precipitation data from Central Park weather observations...

How to parallelize your LLM inference calls with Bodo

Large Language Models (LLMs) can be computationally intense and inference speed can quickly become a bottleneck—especially when you're sending multiple queries and waiting for responses. Slow response times don’t just lead to delays; they make real-time applications impractical, drive up compute costs, and create scalability challenges...

Bodo vs. Numba: Choosing the Right Tool for High-Performance Python

When it comes to efficient number crunching in Python, two powerful tools are Bodo and Numba. Both aim to speed up Python code using compilation techniques. While there is some overlap in the domains they support, there are notable performance differences, and each has unique areas of support that the other does not...

Bodo ♥️ PyIceberg: Scalable IO with the Simplicity of Python

Apache Iceberg is an open table format that provides simple, reliable, and scalable tables that are interoperable across various data tools such as compute engines. At Bodo, we believe that Iceberg is the future of data warehouse and data lake solutions due to its flexibility and open-source backing. However, using Iceberg at a large scale often requires Java-based tools which are difficult for developers to use and maintain.

Parallel FASTA Aggregation with Bodo: A Python Example for Scalable Bioinformatics

In modern bioinformatics, large-scale genomic data is the norm, requiring efficient ways to read, process, and combine massive collections of sequence files for meaningful analyses. Reading multiple large FASTA files—a routine step in genomics—can become a bottleneck when datasets scale. This post shows how to efficiently parse and combine FASTA sequences in parallel using Bodo...