When To Use Bodo

The Bodo platform powers fast, efficient big data processing for Python data teams.
Bodo’s Inferential Compiler delivers high-performance style computing for data-intensive processing.

Where Bodo Shines

Bodo is best for data processing intense use-cases, as well as with large dataset processing that benefits from parallelized execution.

Ideal Data Characteristics and Use Cases:

Bodo’s linear scaling capability is most noticeable in efforts involving jobs of 100’s of GBs, hundreds of millions of Dataframe rows, and compute times approaching/exceeding 1 hour.
Pain Points
  • Long Processing Time

  • Complex Re-Writes



  • Lack of Parallel
    Programming Skills

  • Time-to-Market Delays

  • Long jobs -- taking hours or days instead of minutes or seconds, causing missed SLAs.
  • Data teams lose time moving from prototyping to deployment when code needs to be re-written to a different language to achieve better performance.
  • Many Data Engineering staff lack parallel programming skills in Scala, C, Spark, etc
  • When analytics teams sit idle awaiting data from data prep teams, businesses lose agility and competitiveness.
  • Long Processing Time

    Long jobs -- taking hours or days instead of minutes or seconds, causing missed SLAs.
  • Time to Translate to Better
    Performing Languages

    Data teams lose time moving from prototyping to deployment when code needs to be translated to a different language to achieve better performance.
  • Lack of Parallel
    Programming Skills


    Many Data Engineering staff lack parallel programming skills in Scala, C, Spark, etc
  • Lost Time by Analytics
    Team Awaiting Data

    When analytics teams sit idle, awaiting data from data prep teams.
Use Cases
  • Data Prep and ETL

  • MI Model Training

  • Feature Engineering

  • Exploratory Analysis


  • Data transformation to integrate and format data to be ready for analysis, reporting, and machine learning.
  • Where fast, efficient ingestion and transformation of large data sets is needed.
  • Data analysis to reveal categories, properties, and attributes of data.
  • For big data that includes large and/or repetitive data set analyses.
  • Data Prep and ETL

    Data transformation to integrate and format data to be ready for analysis, reporting, and machine learning.
  • Ml Model Training

    Where fast, efficient ingestion and transformation of large data sets is needed.
  • Feature Engineering

    Data analysis to reveal categories, properties, and attributes of data.
  • Exploratory Analysis

    For big data that includes large and/or repetitive data set analyses.
Optimized Python Libraries:
Bodo compiles functions into efficient native parallel binaries, where the operations are optimized by Bodo. Optimized libraries include:
  • Pandas

  • Numpy

  • Machine Learning

  • Deep Learning


    Minimum Requirements:
    Basic CPUs (e.g., on-premises, AWS, Google Cloud, Azure). Bodo does not require any special-purpose hardware or networking.
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