/** @type {import("prettier").Config} */
const config = {
  printWidth: 120,
};

module.exports = config;


Primitive types

Time types

Identifier types

Geospatial types

Learn about the data types supported by Tilebox

Data types

Tilebox Docs

The Solar System's #1 developer tool for space data management

Introduction

Tilebox

This guide helps you set up and get started using Tilebox. It covers how to install a Tilebox client for your preferred language and how to use it to query data from a dataset and run a workflow.

Quickstart

Explore your datasets and workflows with the Tilebox Console

Console

Large Language Models (LLMs) are powerful tools for exploring and learning about Tilebox. This section explains how to provide them with Tilebox-specific context for tailored, relevant and up-to-date responses.

AI Assistance

To access the Tilebox API, you must authenticate your requests. This guide explains how authentication works, focusing on API keys used as bearer tokens.

Authentication

Time series data

Learn about time series dataset collections

Collections

Learn how to load data from Time Series Dataset collections.

Loading Data

Loading Time Series Data

Learn how to ingest and delete data from Time Series Dataset collections.

Ingesting and deleting data

Ingesting and Deleting Data

Learn about the Open data available in Tilebox.

Open Data

Learn about the different storage clients available in Tilebox to access open data.

Storage Clients

The Tilebox workflow orchestrator is a parallel processing engine. It simplifies the creation of dynamic tasks that can be executed across various computing environments, including on-premise and auto-scaling clusters in public clouds.

Tilebox Workflows

Sharing data between tasks is crucial for workflows, especially in satellite imagery processing, where large datasets are the norm. Tilebox Workflows offers a straightforward API for storing and retrieving data from a shared cache.

Caches

Client

Client.datasets

Client.dataset

Dataset.collections

Dataset.create_collection

Dataset.get_or_create_collection

Dataset.collection

Collection.info

Collection.load

Collection.find

Collection.delete

Collection.delete_ids

Task

Client.runner

TaskRunner.run_all

TaskRunner.run_forever

Context.submit_subtask

Context.job_cache

JobCache.group

JobCache.__iter__

JobClient.submit

JobClient.retry

JobClient.cancel

JobClient.visualize

Book a Demo

Discord

Documentation

API Reference

Website

Installation

Sample code maintained to use and learn from.

Sample notebooks

Overview of the Xarray library, common use cases, and implementation details.

Xarray

Tilebox offers a standard synchronous API by default but also provides an async client option when needed.

Async support

How geometries are handled in the Tilebox Python client.

Geometries

Creating a dataset

Tasks

Understanding and Creating Tasks

Jobs

Task Runners

Clusters

Integrate OpenTelemetry into your Tilebox Workflows

OpenTelemetry

OpenTelemetry Integration

Record the execution of your workflow tasks as OpenTelemetry traces and spans

Tracing

Set up distributed logging using the OpenTelemetry logging protocol

Logging

Process data in near-real-time by triggering jobs based on external events

Automations

Cron triggers

Trigger jobs after objects are created or modified in a storage location

Get Started

SDKs

Datasets

Workflows

Data types

Primitive types

Time types

Identifier types

Geospatial types

Get Started

SDKs

Datasets

Workflows

​Primitive types

​Time types

​Identifier types

​Geospatial types

Primitive types

Time types

Identifier types

Geospatial types