July 2021 Summaries

The field of data science has seen tremendous growth with Python becoming the de facto language for connecting data science. However, due to Python's limitations, scaling code can be a steep learning curve, often requiring rewriting native Python source code in another architecture. This bottleneck hinders development and can lead to companies being outcompeted by rapidly changing data science landscapes. The Bodo architecture simplifies infrastructure by unifying production and development environments through a single source code, software stack, and dataset, utilizing open-source Numba Just-In-Time compiler technology and high-performance computing technologies. This solution enables productive data science, accurate insights, and cost-effective infrastructure through unparalleled efficiency, and is optimized for real-time insights from Cloud to Edge. Bodo allows deployment of native Python frameworks on cloud infrastructures without code rewrites, and offers features such as running different workloads on various environments, being fully portable, and adapting to emerging complexity.

The `apply` function in Pandas can be slow when working with big data, but Bodo offers a faster alternative solution that doesn't require major changes to code or learning new skills. Bodo's performance is unmatched and it provides unlimited scalability compared to other solutions like Spark. A comparison of the `apply` function executed on a Pandas DataFrame in Pandas and Bodo was conducted using the New York City parking violation dataset, which is notorious for being big data that data scientists struggle with. The analysis showed a 30X improvement in performance when using Bodo compared to Pandas, making it a modern solution to speed up data wrangling without modifying source code. Bodo has achieved significant milestones in 2022, including simplifying data management with the Iceberg connector and creating ETL pipelines, demonstrating its potential as an open data warehouse of the future.