Python is the language of choice for AI and machine learning (ML), but SQL has been used...
Bodo allows machine learning practitioners to rapidly explore data and build complex pipelines. Using Bodo, developers can seamlessly scale their codes from using their own laptop to using Bodo's platform. In this series, we will...
In this post, we will run the code using Bodo. It means that the data will be distributed in chunks across processes. Bodo's documentation provides more information about the parallel execution model. If you want to run the example using Pandas only (without Bodo), simply...
Bodo’s mission is to enable easy access to high-performance computing; to build a platform that makes working with petabyte-scale datasets as fast and straightforward as running pandas on small datasets using a laptop. We believe...
The Snowflake Data Cloud simplifies data management for data engineers at a near-unlimited scale, while the Bodo Platform brings extreme performance and scalability to large-scale Python data processing. Snowflake and Bodo have combined forces to give data teams...
Overheads of master-executor systems like Spark have been justified as a “necessary evil” for achieving resilience. However, we have shown that Bodo can achieve much higher resilience without extra overheads.
Amazon S3 is one of the most popular technologies that data engineers use to store data as a data lake. One of the typical applications is to read compressed parquet files as part of the extract process in an ETL (extract-transform-load) pipeline...
For a long time, the python multiprocessing library has been a solution for many data scientists and engineers to get faster results when processing time is a pain point. I want to show you a much faster solution: Bodo.
Today I’m happy to announce our collaboration with Xilinx, Inc., including their taking an investment in Bodo. This will be meaningful to more than just Xilinx and Bodo customers... It signifies another stage of our democratizing access to large-scale parallel computing using Python.
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