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The Python library for data science and AI that runs where your data lives.

.. container:: vob-hero-cta .. button-ref:: getting_started :ref-type: ref :color: primary Get started .. button-ref:: connection :ref-type: ref :color: secondary Connect to VAST .. button-ref:: api :ref-type: ref :color: secondary Browse the API .. raw:: html
============================== In-database data science & AI ============================== VAST Orbit lets data scientists, analysts, and ML engineers run a complete workflow — preparing, exploring, analyzing, and modeling data — directly inside the VAST Data Platform, using the pandas- and scikit-learn-style API they already know. Instead of copying data out to a notebook, VAST Orbit pushes the work down to where the data sits and brings only the answers back, so the same code runs on a kilobyte or a petabyte, against a table or a Parquet file, without a rewrite. .. raw:: html
What VAST Orbit brings to your business --------------------------------------- Most data science works backwards: the data is huge and stays put, while the tools are small and insist everything be copied to them. VAST Orbit removes the copy — preparation, analytics, and scoring all run inside VAST — and that single change pays off differently for everyone who touches the data. .. tab-set:: .. tab-item:: For data scientists .. image:: _static/website/about_us/cover/data_scientist_dashboard.png :class: about-cover :alt: Familiar Python, at any scale .. raw:: html Your tools and your syntax, at any scale. Keep the pandas and ``scikit-learn`` habits you already have. ``VastFrame`` behaves like a DataFrame and the models follow the ``scikit-learn`` API, so the notebook you write against a sample becomes production code against the full table — no rewrite, no new language, and no waiting on an export before you can think. Because the work runs in VAST, the size of the data stops being your problem: the same few lines hold whether you are working through a megabyte or a petabyte. .. tab-item:: For platform & data teams .. image:: _static/website/about_us/cover/platform.png :class: about-cover :alt: One governed surface .. raw:: html One governed surface instead of pipeline sprawl. Since the work executes where the data lives, there are no extracts to copy and no side pipelines to babysit. Sensitive data stays inside VAST under one set of controls, lineage stays intact, and the platform you already run becomes the single place analysts, models, and agents all work. That means far less to secure, move, and reconcile — and far fewer moving parts between a question and its answer. .. tab-item:: For the business .. image:: _static/website/about_us/cover/data_scientist_business.png :class: about-cover :alt: Answers sooner, for less .. raw:: html Answers sooner, on more of your data, for less. Every copy is time lost, compute and storage paid for twice, and another place data can leak. Removing it means questions are answered sooner, against more of the estate at once, at a fraction of the infrastructure cost. A central data strategy stops being an architecture diagram and becomes something teams use day to day — on a single table or every asset across the business. Key features ------------ .. grid:: 1 2 3 3 :gutter: 3 :class-container: feature-tiles .. grid-item-card:: |i-indb| No data movement :text-align: center Preparation, analytics, and scoring execute inside VAST. Your data never leaves the platform, so work stays fast, governed, and secure. .. grid-item-card:: |i-frame| Python you already know :text-align: center ``VastFrame`` behaves like a pandas DataFrame and the models follow the ``scikit-learn`` API, so notebook code becomes production code unchanged. .. grid-item-card:: |i-multisource| One query, every source :text-align: center One federated query joins VAST tables, data-lake files, and external databases — with no ETL pipeline to build first. .. grid-item-card:: |i-scale| Scale without rewrites :text-align: center VAST's flash-native, disaggregated architecture keeps queries interactive from gigabytes to exabytes, so workflows grow with your data. .. grid-item-card:: |i-inml| Machine learning in place :text-align: center Train on intelligently sampled data, then deploy the model for in-database inference that scores billions of rows where they live. .. grid-item-card:: |i-magic| Ready for AI agents :text-align: center A built-in MCP server and the ``%%ai`` and ``%%sql`` notebook magics let assistants and LLMs query and reason over your VAST data directly. What you can do with it ----------------------- .. tab-set:: .. tab-item:: Prepare & explore .. image:: _static/website/about_us/cover/indb_dashboard.png :class: about-cover :alt: Prepare and explore data in VAST .. raw:: html Clean, shape, and understand data at any scale. Cleaning and shaping data is usually the slowest part of a project, and it only gets slower as the data grows. With VAST Orbit you handle missing values, remove duplicates, normalize, encode, and engineer features using familiar calls that execute inside VAST — so a petabyte table feels like a megabyte one and nothing is pulled into Python first. When it is time to look before you leap, charts and statistical summaries are generated from intelligent samples, letting you profile distributions, correlations, and outliers across billions of rows in seconds rather than waiting on an export. .. tab-item:: Analyze across sources .. image:: _static/website/about_us/cover/different_sources.png :class: about-cover :alt: Analyze across every source .. raw:: html Join everything, in one query, close to the data. Real questions rarely live in a single table. Because VAST Orbit reaches your data through one federated query engine, one piece of Python can join a VAST table with Parquet files in the data lake and a customer record in PostgreSQL — all in the same query and all executed next to the data. That turns the idea of a central data layer from an architecture diagram into something an analyst can use day to day: ad-hoc analysis across the whole estate, with no ETL job to schedule and no warehouse to load first. .. tab-item:: Machine learning .. image:: _static/website/about_us/cover/healthcare.png :class: about-cover :alt: Machine learning in the database .. raw:: html Train flexibly, then score billions of rows in place. VAST Orbit supports a hybrid workflow that matches how teams really work: train quickly with the embedded algorithms or bring a ``scikit-learn`` model you trained locally, then deploy it for inference that runs as SQL inside VAST. Scoring happens where the data is, so you can refresh predictions on live data continuously — fraud scores, churn risk, demand forecasts — instead of shuttling features and results between systems and serving infrastructure. .. tab-item:: AI platform .. image:: _static/website/about_us/cover/ai_development_platform.png :class: about-cover :alt: An AI development platform on VAST .. raw:: html Make your data a first-class tool for AI. Modern AI is only as good as its access to context. VAST Orbit exposes your data and its own analytics as tools an LLM can call through an MCP server, and the ``%%ai`` magic lets you ask a question in plain language and get back a working, schema-aware query. The result is a single governed platform where analysts, models, and agents all operate on the same data, with no copies in between — the foundation for AI applications you can actually trust in production. Use cases --------- The same library powers very different problems, because they all come down to running analytics and models close to large, fast-moving data. .. grid:: 1 2 3 3 :gutter: 3 :class-container: feature-tiles .. grid-item-card:: |i-fraud| Fraud detection :text-align: center Score every transaction against historical patterns in place and catch fraud in real time — without exporting sensitive financial data. .. grid-item-card:: |i-churn| Customer 360 & churn :text-align: center Combine CRM, billing, and clickstream into one view and predict churn where the data already sits, then refresh scores continuously. .. grid-item-card:: |i-telecom| Telecom & networks :text-align: center Optimize coverage and capacity over billions of call-detail records, joining live traffic with historical patterns in a single query. .. grid-item-card:: |i-energy| IoT & asset health :text-align: center Monitor sensor streams and forecast failures across live and historical readings together, scoring devices where their data lands. .. grid-item-card:: |i-insurance| Risk & insurance :text-align: center Price risk and flag anomalies across policy, claims, and external data sets at once, with full lineage and no data leaving the platform. .. grid-item-card:: |i-bio| Life sciences :text-align: center Explore genomic and clinical data at petabyte scale with familiar Python, keeping regulated data governed inside VAST throughout. A quick look ------------ Connect once, then prepare, join, visualize, and model — every step executing in VAST: .. code-block:: python import vastorbit as vo # Connect to VAST vo.new_connection({ "host": "vast-cluster.example.com", "port": 8080, "catalog": "vast_catalog", "schema": "analytics", }) # A VastFrame is a handle to data in VAST — nothing is pulled into Python customers = vo.VastFrame("vast_catalog.crm.customers") # Prepare the data, in place customers = customers.fillna({"income": 0, "age": customers["age"].avg()}) customers["income_norm"] = customers.normalize("income") # Explore with intelligent sampling customers["age"].hist(nbins=20) # Join a VAST table with data-lake files — one query, executed in VAST transactions = vo.VastFrame("hive.default.transactions") enriched = customers.join(transactions, on="customer_id", how="inner") # Train a model, then score billions of rows in the database from vastorbit.machine_learning.vast import RandomForestClassifier model = RandomForestClassifier(n_estimators = 4) model.fit(enriched, ["age", "tenure"], "churn") predictions = model.predict(enriched) # runs inside VAST Built on the VAST Data Platform ------------------------------- VAST Orbit is only as capable as the foundation beneath it. The VAST Data Platform unifies storage, database, and compute into one consistent system, so every asset — transactional tables, data-lake files, streaming events, and vector embeddings — lives in one place and speaks one language. When the infrastructure is that consistent, everything becomes possible: there is no copy step to slow you down, no second system to reconcile, and no scale ceiling to design around. VAST Orbit turns that consistency into a single queryable surface for Python, where one ``VastFrame`` reaches a table or a file, a gigabyte or an exabyte, all the same way. It works with **VAST 4.5 and later**. Learn more about the foundation it builds on at the `VAST Data Platform `__. Today that single query runs on Trino; VAST's own query engine is on the way and will become the default. Because you work through one ``VastFrame`` API, your code stays exactly the same when the engine underneath it changes. Installation ------------ Install the beta with pip: .. code-block:: bash pip install vastorbit VAST Orbit needs **Python 3.12+**, network access to your VAST cluster, and **VAST 4.5 or later**. Version 0.1.x is a beta; a production-ready 1.0.0 is on the way. See :ref:`getting_started` for full setup and :ref:`connection` for connection and authentication options. .. note:: Placeholders such as ``vast-cluster.example.com`` and ``vast_catalog`` stand in for your own cluster host and catalog names throughout the docs. Explore the documentation -------------------------- .. grid:: 2 2 2 2 :gutter: 3 :class-container: feature-tiles .. grid-item-card:: |i-start| Getting Started :link: getting_started :link-type: ref :text-align: center Install VAST Orbit and run your first in-database query in minutes. .. grid-item-card:: |i-connect| Connection Guide :link: connection :link-type: ref :text-align: center Connect to VAST, authenticate, and reach every catalog. .. grid-item-card:: |i-guide| User Guide :link: user_guide :link-type: ref :text-align: center Master ``VastFrame`` and the in-database operations behind it. .. grid-item-card:: |i-ml| Machine Learning :link: api.machine_learning :link-type: ref :text-align: center Train models and deploy them for inference at production scale. .. grid-item-card:: |i-charts| Chart Gallery :link: chart_gallery :link-type: ref :text-align: center See the visualizations VAST Orbit can build from sampled data. .. grid-item-card:: |i-stats| Statistics :link: statistics :link-type: ref :text-align: center The library by the numbers — measured live from the source. .. grid-item-card:: |i-examples| Examples :link: examples :link-type: ref :text-align: center Follow end-to-end tutorials across analytics and ML workflows. .. grid-item-card:: |i-about| About Us :link: about_us :link-type: ref :text-align: center Meet the people behind VAST Orbit and the story that shaped it. .. note:: VAST Orbit brings Python data science to the VAST Data Platform — query anywhere, analyze everything, and build AI at any scale, all with in-database execution and zero data movement. .. toctree:: :hidden: :maxdepth: 1 :titlesonly: getting_started connection whats_new contribution_guidelines examples api chart_gallery user_guide statistics about_us