diff --git a/datasets/concepts/collections.mdx b/datasets/concepts/collections.mdx index 13d2d32..ac72ed3 100644 --- a/datasets/concepts/collections.mdx +++ b/datasets/concepts/collections.mdx @@ -188,8 +188,8 @@ if err != nil { ## Next steps - + Learn how to query data from a collection. - + diff --git a/datasets/concepts/datasets.mdx b/datasets/concepts/datasets.mdx index c94b89b..4ce70dc 100644 --- a/datasets/concepts/datasets.mdx +++ b/datasets/concepts/datasets.mdx @@ -10,14 +10,14 @@ icon: database ## Related Guides - + Learn how to create a Timeseries dataset using the Tilebox Console. Learn how to ingest an existing CSV dataset into a Timeseries dataset collection. - + ## Dataset types @@ -28,7 +28,7 @@ or additionally also spatially filtered queries. To find out which fields are required for each dataset type check out the documentation for the available dataset types below. - + Each data point is linked to a specific point in time. Common for satellite telemetry, or other time-based data. Supports efficient time-based queries. @@ -37,7 +37,7 @@ below. Each data point is linked to a specific point in time and a location on the Earth's surface. Common for satellite imagery. Supports efficient time-based and spatially filtered queries. - + ## Dataset specific fields diff --git a/datasets/introduction.mdx b/datasets/introduction.mdx index 36c1174..27831a5 100644 --- a/datasets/introduction.mdx +++ b/datasets/introduction.mdx @@ -12,7 +12,7 @@ explore any of the wide range of [available public open data datasets](/datasets Learn more about datasets by exploring the following sections: - + Learn what dataset types are available on Tilebox and how to create, list and access them. @@ -25,7 +25,7 @@ Learn more about datasets by exploring the following sections: Learn how to ingest data into a collection. - + For a quick reference to API methods or specific parameter meanings, [check out the complete datasets API Reference](/api-reference/python/tilebox.datasets/Client). @@ -169,8 +169,8 @@ func main() { ## Next steps - + - + diff --git a/guides/datasets/create.mdx b/guides/datasets/create.mdx index fe01df4..25a4e90 100644 --- a/guides/datasets/create.mdx +++ b/guides/datasets/create.mdx @@ -8,14 +8,14 @@ This page guides you through the process of creating a dataset in Tilebox using ## Related documentation - + Learn about Tilebox datasets and how to use them. Learn about Timeseries datasets, which link each data point to a specific point in time. - + ## Creating a dataset in the Console diff --git a/guides/datasets/ingest.mdx b/guides/datasets/ingest.mdx index 1efe1e5..82f4cc8 100644 --- a/guides/datasets/ingest.mdx +++ b/guides/datasets/ingest.mdx @@ -4,11 +4,11 @@ description: Learn how to ingest an existing dataset into Tilebox icon: up-from-bracket --- - + This guide is also available as a Google Colab notebook. Click here for an interactive version. - + This page guides you through the process of ingesting data into a Tilebox dataset. Starting from an existing dataset available as file in the [GeoParquet](https://geoparquet.org/) format, you'll go through the process of @@ -20,14 +20,14 @@ ingesting that data into Tilebox as a [Timeseries](/datasets/types/timeseries) d ## Related documentation - + Learn about Tilebox datasets and how to use them. Learn how to ingest data into a Tilebox dataset. - + ## Downloading the example dataset @@ -234,7 +234,7 @@ on one of the data points. Congrats. You've successfully ingested data into Tilebox. You can now explore the data in the console and use it for further processing and analysis. - + Learn all about [querying your newly created dataset](https://docs.tilebox.com/datasets/query) @@ -244,4 +244,4 @@ further processing and analysis. Check out a growing number of publicly available open data datasets on Tilebox - + diff --git a/guides/workflows/multi-language.mdx b/guides/workflows/multi-language.mdx index 1b774e4..606cffc 100644 --- a/guides/workflows/multi-language.mdx +++ b/guides/workflows/multi-language.mdx @@ -4,11 +4,11 @@ description: Learn how to create workflows that use tasks written in different l icon: diagram-project --- - + The code for this guide is available on GitHub. - + ## Tilebox languages and SDKs @@ -205,11 +205,11 @@ Image captured for [40.75, -73.98] with 30m resolution and bands [489, 560.6, 66 ## Next Steps - + The code for this guide is available on GitHub. - + As a learning exercise, you can try to change the [News API Workflow](/workflows/concepts/tasks#dependencies-example) to replace the `FetchNews` task with a Go task and keep all the other tasks in Python. You'll learn how to submit a subtask in another language than what the current task is executed in. diff --git a/introduction.mdx b/introduction.mdx index 1b557a7..cf86e74 100644 --- a/introduction.mdx +++ b/introduction.mdx @@ -9,13 +9,13 @@ mode: wide import { HeroCard } from '/snippets/components.mdx'; -Tilebox is a space data native distributed computing tool. It provides a framework that simplifies access, processing, and distribution of space data across different environments enabling efficient multi-language, multi-cluster workflows. Tilebox integrates seamlessly with your existing infrastructure, ensuring that you maintain complete control over your data and algorithms. +Tilebox is a lightweight space data management and orchestration software - on ground and in orbit. It provides a framework that simplifies access, processing, and distribution of space data across different environments enabling efficient multi-language, multi-cluster workflows. Tilebox integrates seamlessly with your existing infrastructure, ensuring that you maintain complete control over your data and algorithms. ## Modules Tilebox consists of two primary modules: - + Workflows - + ## Getting Started To get started, check out some of the following resources: - + - + ## Guides You can also start by looking through these guides: - + Find out how to deploy Task Runners to run workflows in a parallel, distributed manner. - + diff --git a/quickstart.mdx b/quickstart.mdx index 219146d..e4823e3 100644 --- a/quickstart.mdx +++ b/quickstart.mdx @@ -101,14 +101,14 @@ If you prefer to work locally, follow these steps to get started. Review the following guides to learn more about the modules that make up Tilebox: - + Learn how to create a Timeseries dataset using the Tilebox Console. Learn how to ingest an existing CSV dataset into a Timeseries dataset collection. - + @@ -304,14 +304,14 @@ If you prefer to work locally, follow these steps to get started. Review the following guides to learn more about the modules that make up Tilebox: - + Learn how to create a Timeseries dataset using the Tilebox Console. Learn how to ingest an existing CSV dataset into a Timeseries dataset collection. - + diff --git a/sdks/go/examples.mdx b/sdks/go/examples.mdx index 54fa7d7..b0466de 100644 --- a/sdks/go/examples.mdx +++ b/sdks/go/examples.mdx @@ -11,7 +11,7 @@ More examples can be found throughout the docs. ## Workflows examples - + How to use Tilebox Workflows to submit and execute a simple task. @@ -24,15 +24,15 @@ More examples can be found throughout the docs. How to set up tracing and logging for workflows using OpenTelemetry. - + ## Datasets examples - + How to query datapoints from a Tilebox dataset. How to create a collection, ingest datapoints, and then delete them. - + diff --git a/sdks/go/install.mdx b/sdks/go/install.mdx index 317d441..e7ba6e9 100644 --- a/sdks/go/install.mdx +++ b/sdks/go/install.mdx @@ -8,14 +8,14 @@ icon: download Tilebox offers a Go SDK for accessing Tilebox services. It additionally includes a command-line tool (tilebox-generate) that can be installed separately. - + Datasets and workflows client for Tilebox Command-line tool to generate Tilebox datasets types for Go - + ## Installation diff --git a/sdks/introduction.mdx b/sdks/introduction.mdx index d6ef858..1951c71 100644 --- a/sdks/introduction.mdx +++ b/sdks/introduction.mdx @@ -9,7 +9,7 @@ import { HeroCard } from '/snippets/components.mdx'; The following language SDKs are currently available for Tilebox. Select one to learn more. - + Tilebox Go - + diff --git a/sdks/python/install.mdx b/sdks/python/install.mdx index a93461f..4a4574f 100644 --- a/sdks/python/install.mdx +++ b/sdks/python/install.mdx @@ -8,14 +8,14 @@ icon: download Tilebox offers a Python SDK for accessing Tilebox services. The SDK includes separate packages that can be installed individually based on the services you wish to use, or all together for a comprehensive experience. - + Access Tilebox datasets from Python Workflow client and task runner for Tilebox - + ## Installation diff --git a/sdks/python/xarray.mdx b/sdks/python/xarray.mdx index eea7ea3..9a5475b 100644 --- a/sdks/python/xarray.mdx +++ b/sdks/python/xarray.mdx @@ -346,7 +346,7 @@ This section covers only a few common use cases for Xarray. The library offers m Some useful capabilities not covered in this section include: - + - + diff --git a/snippets/components.mdx b/snippets/components.mdx index 968f53c..93539bb 100644 --- a/snippets/components.mdx +++ b/snippets/components.mdx @@ -6,7 +6,7 @@ export const HeroCard = ({ children, title, description, href }) => { > {children}

{title}

-

{description}

+

{description}

); }; diff --git a/storage/clients.mdx b/storage/clients.mdx index b51a5bd..5cb3621 100644 --- a/storage/clients.mdx +++ b/storage/clients.mdx @@ -164,7 +164,7 @@ Contents: ### Further Reading - + - + ## Umbra Space diff --git a/workflows/caches.mdx b/workflows/caches.mdx index e4b36d5..883e670 100644 --- a/workflows/caches.mdx +++ b/workflows/caches.mdx @@ -38,12 +38,12 @@ attribute that can be used to [store and retrieve data](#storing-and-retrieving- Tilebox Workflows comes with four cache implementations out of the box, each backed by a different storage system. - + - + ### Google Storage Cache diff --git a/workflows/introduction.mdx b/workflows/introduction.mdx index 0033a90..96b1575 100644 --- a/workflows/introduction.mdx +++ b/workflows/introduction.mdx @@ -8,7 +8,7 @@ mode: wide This section provides guides showcasing how to use the Tilebox workflow orchestrator effectively. Here are some of the key learning areas: - + Create tasks using the Tilebox Workflow Orchestrator. @@ -27,7 +27,7 @@ This section provides guides showcasing how to use the Tilebox workflow orchestr Trigger jobs based on events or schedules, such as new data availability or CRON schedules. - + ## Terminology diff --git a/workflows/near-real-time/automations.mdx b/workflows/near-real-time/automations.mdx index 52c39c9..6410821 100644 --- a/workflows/near-real-time/automations.mdx +++ b/workflows/near-real-time/automations.mdx @@ -15,14 +15,14 @@ By defining trigger conditions, you can automatically submit jobs based on exter Tilebox Workflows currently supports the following trigger conditions: - + Trigger jobs based on a Cron schedule. Trigger jobs after objects are created or modified in a storage location such as a cloud bucket. - + Dataset Event Triggers, which will trigger jobs when new data points are ingested into a Tilebox dataset, are on the roadmap. Stay tuned for updates. @@ -78,10 +78,10 @@ print(automations) To register an automation, use the `create_*_automation` methods specific to each trigger type provided by the automation client. Refer to the documentation for each trigger type for more details. - + - + ## Overview in the Tilebox Console diff --git a/workflows/near-real-time/storage-events.mdx b/workflows/near-real-time/storage-events.mdx index b227bee..5447bfc 100644 --- a/workflows/near-real-time/storage-events.mdx +++ b/workflows/near-real-time/storage-events.mdx @@ -42,11 +42,11 @@ class LogObjectCreation(StorageEventTask): Storage Event tasks are triggered when objects are created or modified in a storage location. This location can be a cloud storage bucket or a local file system. Tilebox supports the following storage locations: - + - + ### Registering a Storage Location diff --git a/workflows/observability/open-telemetry.mdx b/workflows/observability/open-telemetry.mdx index 17fb7ef..49b2bce 100644 --- a/workflows/observability/open-telemetry.mdx +++ b/workflows/observability/open-telemetry.mdx @@ -9,7 +9,7 @@ icon: telescope Effective observability is essential for building reliable workflows. Understanding and monitoring the execution of workflows and their tasks helps ensure correctness and efficiency. This section describes methods to gain insights into your workflow's execution. - + - + ## OpenTelemetry