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
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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
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Long jobs -- taking hours or days instead of minutes or seconds, causing missed SLAs.
Time to Translate to Better
Performing Languages
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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
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Where fast, efficient ingestion and transformation of large data sets is needed.
Feature Engineering
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Data analysis to reveal categories, properties, and attributes of data.
Exploratory Analysis
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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

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Numpy

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Machine Learning

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Deep Learning

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Bodo optimizes most common operations.

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Bodo can optimize and parallelize most Numpy operators.
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We are constantly expanding our support for Scikit-learn and XGBoost.Contact us for more details and support
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If you use PyTorch and TensorFlowcontact us for more details and support.
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Pandas
Bodo optimizes most common operations.
Numpy
Bodo can optimize and parallelize most Numpy operators.
Machine Learning
We are constantly expanding our support for Scikit-learn and XGBoost.Contact us for more details and support
Deep Learning
If you use PyTorch and TensorFlowcontact us for more details and support.