Getting Started

To quickly get started with querying data, we first need to know which content (datasets or data tables) the data lake contains. This will help us understand how to effectively query and utilize the available data resources.

Python Client

We recommend using the Beacon Python Client for interacting with the Beacon Data Lake API. It provides a simple and structured way to query and manage data.

Python Client

You can use the Beacon Python API Client to interact with the Beacon Data Lake API using Python.

Documentation

The documentation for the Beacon Python API Client can be found at: https://maris-development.github.io/beacon-py/

Source Code

The source code for the Beacon Python API Client is available on GitHub: https://github.com/maris-development/beacon-py

The Beacon Python API Client can be installed using pip:

pip install beacon-api

Rest-API

Beacon provides a RESTful API for working with the data lake. Libraries are available for various programming languages to simplify the process of making API calls. Eg. the beacon-api Python package.

Querying Datasets

To learn which datasets are available use the following http GET request:

GET /api/datasets

This will return a list of datasets available in the data lake.

Once we know which datasets are available, we can list the schema of the datasets using the following http GET request: The schema will provide information about the structure of the datasets, including the names and types of the columns.

GET /api/dataset-schema?file=example1.nc

Querying Data Tables

To learn which data tables are available use the following http GET request:

GET /api/tables

This will return a list of data tables available in the data lake. Once we know which data tables are available, we can list the schema of the data tables using the following http GET request:

The schema will provide information about the structure of the data tables, including the names and types of the columns.

GET /api/table-schema?table=example1