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Version 0.1.0 (Beta)

🎉 First Beta Release
February 2026 - Beta Release

Welcome to the first release of VAST Orbit - Python data science for VAST DataBase!

Important

Beta Status: VAST Orbit 0.1.0 is in beta. Production-ready version 1.0.0 coming soon. API may change based on feedback. Report issues at `vastsupport.slack.com <https://vastsupport.slack.com>`__.

Release Highlights

Python Data Science for VAST

VAST Orbit 0.1.0 brings complete data science workflows to VAST DataBase - prepare, explore, analyze, and build ML models with in-database execution.

Core Features:

  • Data Preparation in VAST - Clean, transform, engineer features at any scale

  • Interactive Exploration - Charts and visualizations with intelligent sampling

  • 400+ Functions - Complete analytics toolkit executing in VAST

  • 10 ML Algorithms - Embedded models for training and inference

  • Multi-Source Access - Query VAST tables, files, and external databases

  • Zero Data Movement - All processing in VAST DataBase

What’s Included

Data Preparation:

  • fillna, dropna, drop_duplicates - cleaning in VAST

  • normalize, scale, encode - transformations in-database

  • Feature engineering at petabyte scale

  • Statistical profiling and quality checks

Interactive Exploration:

  • Histograms, scatter plots, correlation matrices

  • Box plots, violin plots, KDE

  • Intelligent sampling for instant visualization

  • Statistical analysis (mean, median, variance, quantiles)

Analytics:

  • 400+ functions executing in VAST

  • pandas-like DataFrame operations

  • Aggregations, joins, window functions

  • Time series and geospatial analysis

Machine Learning:

  • 10 embedded models (RandomForest, GradientBoosting, LinearRegression, etc.)

  • sklearn model import support

  • In-database inference at scale

  • Production-ready deployment

Core Modules:

  • vastorbit.VastFrame - pandas-like DataFrame for VAST

  • vastorbit.machine_learning.vast - ML algorithms

  • vastorbit.plot - Visualization library

  • vastorbit.sql - SQL execution utilities

  • vastorbit.stats - Statistical functions

Supported Platforms:

  • Python 3.12+

  • Linux and macOS

  • VAST DataBase 5.0.0-sp10 or later

Example Usage:

import vastorbit as vo

# Connect to VAST DataBase
vo.new_connection({
    'host': 'vast-cluster.com',
    'catalog': 'vast_catalog'
})

# Query data
vdf = vo.VastFrame('sales_data')

# Data preparation - all in VAST
vdf = vdf.fillna({'revenue': 0})
vdf = vdf.drop_duplicates()

# Explore with charts
vdf['revenue'].hist(nbins=20)
vdf.scatter(['sales', 'revenue'])

# Analyze
summary = vdf.groupby(['region'], ['sum(revenue) AS total'])

# Train ML model
from vastorbit.machine_learning.vast import RandomForestClassifier
model = RandomForestClassifier(n_estimators = 4)
model.fit(vdf, ['feature1', 'feature2'], 'target')

# In-database inference
predictions = model.predict(vdf)

Key Capabilities

In-Database Data Preparation:

  • Clean and transform data directly in VAST

  • Handle missing values, outliers, duplicates

  • Feature engineering at any scale

  • Statistical profiling and validation

Interactive Exploration:

  • Generate charts with intelligent sampling

  • Analyze distributions and correlations

  • Discover patterns and anomalies

  • Visualize billions of rows instantly

Multi-Source Analytics:

  • Query VAST tables and files

  • Access external databases (PostgreSQL, MySQL, MongoDB)

  • Join across sources

  • Unified Python API

In-Database ML:

  • 10 embedded algorithms ready to use

  • Import sklearn models

  • In-database inference in VAST

  • Production-scale scoring

Beta Limitations

As a beta release:

  • API may change before 1.0.0

  • Documentation actively expanding

  • Some advanced features in development

  • Feedback welcome for improvements

Getting Started

Installation:

pip install vastorbit

Documentation:

Support:

Roadmap to 1.0.0

Production Release Plans:

  • API stabilization based on beta feedback

  • Expanded documentation and tutorials

  • Additional ML algorithms

  • Enhanced data preparation functions

  • Advanced visualization capabilities

  • Performance optimizations

  • Production hardening

We’re excited to hear your feedback as we work toward 1.0.0!

Thank You

Thank you for being an early adopter of VAST Orbit. Your feedback shapes the future of data science on VAST DataBase.

Get Involved:

  • Report issues on GitHub

  • Join discussions on Slack

  • Share your use cases

  • Contribute ideas for new features

Happy analyzing with VAST!