Parameters

Errors

Console

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Introduction

Tilebox

This guide will get you all set up and ready to use the Tilebox API. We'll cover how to get started using our python API clients for the data access module as well as the workflow orchestrator.

Quickstart

Check out the Tilebox Console to interactively explore your datasets and workflows

You'll need to authenticate your requests to access the Tilebox API. In this guide, we'll look at how authentication works. Tilebox offers to authenticate your API requests with API keys passed as bearer tokens.

Authentication

Installation

Sample code that is maintained and available to use and learn from.

Sample Notebooks

Xarray library, common use-cases and how they can be implemented easily.

Xarray

In this section we look at async support within the tilebox datasets python client.

Async support

How geometries are handled in the Tilebox python client.

Geometries

Time Series Data

Learn about Time Series Dataset Collections

Collections

Learn about how to load data from Time Series Dataset collections

Loading Data

Loading Time Series Data

Learn about the different storage providers that are available in Tilebox.

Open Data

Tilebox Workflows, or the Tilebox workflow orchestrator is a parallel processing engine that allows an intuitive creation of dynamic tasks that can be parallelized out of the box and executed across compute environments or on-premise as well as in auto-scaling clusters in public clouds.

Tilebox Workflows

Tasks

Understanding and Creating Tasks

Jobs

Task Runners

Clusters

One important aspect of workflows is the ability to share data between tasks. This is especially useful when working with large datasets, as it is often the case in domains such as satellite imagery processing. Tilebox Workflows provides a simple API that allows you to store and retrieve data from a cache that is shared between tasks.

Caches

Integrate Open Telemetry into your Tilebox Workflows

Open Telemetry

Open Telemetry Integration

Record the execution of your workflow tasks as open telemetry traces and spans

Tracing

Set up distributed logging using the open telemetry logging protocol

Logging

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

Recurrent Tasks

Cron Triggers

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

Storage Event Triggers

Listing Datasets

Accessing a Dataset

Listing Collections

Accessing a Collection

How to access information about a collection

Collection Information

How to load a single data point from a collection

Loading a Datapoint

Creating a Storage Client

Downloading Products

Downloading Quicklook Images

Deleting Products

Deleting the Cache

How to download products without using the cache

Datasets

Storage Providers

Workflows

​Parameters

​Errors

Parameters

Errors