/* As deviation from the mint theme: we want to remove the rounded corners */
* {
  border-radius: 0 !important;
}


window.vmtrcq = window.vmtrcq || [];
window.vmtrc = window.vmtrc || function () {
    window.vmtrcq.push(Array.prototype.slice.call(arguments))
};

const script = document.createElement('script');
script.src = 'https://cdn.vemetric.com/main.js';
script.defer = true;
script.setAttribute('data-token', 'rQI52fw6s6frnSOR');
document.head.appendChild(script);


Parameters

Returns

TaskRunner.GetRegiste...

TaskRunner.GetRegisteredTask

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

Tilebox Datasets provides structured and high-performance satellite metadata access.

Tilebox Datasets

Learn how to query and load data from Tilebox datasets.

Query

Querying data

Learn how to ingest data into a Tilebox dataset.

Ingest

Ingesting Data

Learn how to delete data points from Tilebox datasets.

Delete

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

Creating a dataset

Learn how to ingest an existing dataset into Tilebox

Ingesting data

Learn how to ingest data from common file formats into Tilebox

Ingesting from common file formats

Learn how to create workflows that use tasks written in different languages

Multi-language Workflows

Tilebox supports multiple languages and SDKs for accessing datasets and running workflows.

Overview

Tilebox languages and SDKs

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

Examples maintained to use and learn from.

Examples

Overview of protobuf, common use cases, and implementation details.

Protobuf

Product Updates

Tilebox Datasets act as containers for data points. All data points in a dataset share the same type and fields.

Datasets

Collections

Timeseries datasets link each data point to a specific point in time.

Timeseries

Spatio-temporal datasets link each data point to a specific point in time and a location on the Earth's surface.

Spatio-temporal

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

Storage Event Triggers

Client

Client.datasets

Client.dataset

Dataset.collections

Dataset.collection

Dataset.create_collection

Dataset.get_or_create_c...

Dataset.get_or_create_collection

Collection.delete

Collection.find

Collection.info

Collection.ingest

Collection.load

Task

Client.runner

TaskRunner.run_all

TaskRunner.run_forever

Context.submit_subtask

Context.job_cache

ClusterClient.create

ClusterClient.find

ClusterClient.delete

ClusterClient.all

JobCache.group

JobCache.__iter__

JobClient.submit

JobClient.find

JobClient.retry

JobClient.cancel

JobClient.visualize

JobClient.query

Client.Datasets.Get

List

Client.Datasets.List

Collections.Create

Client.Collections.Create

Collections.Get

Client.Collections.Get

Collections.GetOrCreate

Client.Collections.GetOrCreate

Collections.List

Client.Collections.List

Datapoints.GetInto

Client.Datapoints.GetInto

Datapoints.Query

Client.Datapoints.Query

Datapoints.QueryInto

Client.Datapoints.QueryInto

Datapoints.Ingest

Client.Datapoints.Ingest

Datapoints.Delete

Client.Datapoints.Delete

Datapoints.DeleteIDs

Client.Datapoints.DeleteIDs

CollectAs

Collect

GetCurrentCluster

workflows.GetCurrentCluster

SubmitSubtask

workflows.SubmitSubtask

SubmitSubtasks

workflows.SubmitSubtasks

WithTaskSpan

workflows.WithTaskSpan

WithTaskSpanResult

workflows.WithTaskSpanResult

NewTaskRunner

Client.NewTaskRunner

TaskRunner.RegisterTasks

TaskRunner.Run

Clusters.Create

Client.Clusters.Create

Clusters.Get

Client.Clusters.Get

Clusters.Delete

Client.Clusters.Delete

Clusters.List

Client.Clusters.List

Jobs.Submit

Client.Jobs.Submit

Jobs.Get

Client.Jobs.Get

Jobs.Retry

Client.Jobs.Retry

Jobs.Cancel

Client.Jobs.Cancel

Jobs.Query

Client.Jobs.Query

Python

Go

​Parameters

​Returns

Parameters

Returns