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TaskRunner.run_forever

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 ingest data into a Tilebox dataset.

Ingest

Ingesting Data

Learn how to delete data points from Tilebox datasets.

Delete

Deleting Data

Best practices for working with geometries in Tilebox.

Geometries

Working with Geometries

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

Learn how to create a dataset with your own, custom schema

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

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

Learn how to query and load data from Tilebox datasets.

Query

Querying data

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

How geometries are handled in the Tilebox Python client.

Client

Client.datasets

Python

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