Opteryx Documentation

Fast, lightweight SQL analytics for your data. Run queries locally or in the cloud.

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Learning paths

An ordered route through the docs for the job you are doing, ending in a hands-on exercise

ANALYST

13 steps · About 2 hours

Answer questions with SQL in Studio

You write queries and want answers. Someone else worries about tables, tokens and pipelines.

DATA ENGINEER

17 steps · About 3 hours

Build tables that maintain themselves

You own the tables other people query: loading them, shaping them, keeping them fresh and controlling who sees what.

DEVELOPER

13 steps · About 2.5 hours

Call Opteryx from your own code

You are wiring Opteryx into an application, a notebook or a service, and you need auth, a client and a result you can work with.

Getting Started

Set up Opteryx and learn the fundamentals in just a few minutes

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Your first query

A real result in two minutes: in the browser, over OData, or in Python

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Logging in

Set up your account and get access to the platform

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Site tour

Learn your way around the interface and key features

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Load and query data

Import data and run your first query

Core Concepts

Understand the key ideas behind Opteryx

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Access and permissions

Understand how access control works

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Cost model

Learn how costs are calculated

What we guarantee

What Opteryx supports today, and where it stops

✅

Compatibility

Python versions, platforms and formats, verified against the current release

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Known limits

The architectural gaps worth knowing before you commit

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SQL conformance

Which parts of standard SQL are supported, and which aren't

Reference

API and language documentation

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API Reference

Complete API documentation and examples

📝

SQL Language Reference

SQL functions, operators, and statements

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Python Integration

Use Opteryx with Python and SQLAlchemy

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