Bodo is working to create a single data processing workflow orchestration platform for all data types, whether they be media, tabular or semi-structured. Today, coding and processing for different data types are entirely separated. In the future, Bodo dynamic parallel computing architecture will optimize allocation and parallelization of hardware for all types of data processing scenarios, and on all types of data platforms.
Snowflake and Bodo are working together to optimize parallel computing approaches for Python analytics using Snowflake, significantly improving analytics performance, ease of use, and governance while reducing overall analytics costs. This partnership is part of Snowflake’s and Bodo’s commitment to help enterprise data engineers and data scientists simplify access to exceptional performance analytics and machine learning (ML) using the Snowflake Data Cloud.
Xilinx and Bodo are collaborating to develop Accelerator-Level Parallelization (ALP) technology that simplifies Python-based access to highly efficient media processing. Media workflow developers will benefit by being able to code in familiar languages like Python, yet access the highest levels of parallel performance available from FPGA hardware. The partnership builds on Bodo’s existing HPC-style parallelization technology that leverages general-purpose CPUs and clusters to achieve extreme scale past 10,000 cores.