Basic Usage Guide

Introduction

Welcome to the basic usage guide for our platform. This guide will walk you through the essential steps to get you up and running, from initial setup to creating and managing your first project. We aim to provide a clear and concise overview of the fundamental features.

Getting Started

Installation

Before you can start, you need to install our software. Follow these simple steps:

1. Download the latest release from the downloads page.
2. Extract the archive to your desired directory.
3. Run the installer script (if applicable) or ensure the executable is in your system's PATH.

For detailed installation instructions for specific operating systems, please refer to the full installation guide.

Creating Your First Project

Let's create your first project. Open your terminal or command prompt, navigate to the directory where you want to create your project, and run the following command:

init my-awesome-project

This command will create a new directory named my-awesome-project and set up a basic project structure. You can then navigate into your project directory:

cd my-awesome-project

Core Concepts

Configuration

Our platform relies on a configuration file to manage settings. By default, a config.yaml file is created in your project's root directory.

Here's a basic example of a configuration file:

database:
  host: localhost
  port: 5432
  username: admin
  password: secretpassword

server:
  port: 8080
  log_level: info
                

You can modify this file to suit your needs. For a full list of configuration options, see the configuration reference.

Data Handling

Data is at the heart of many applications. Our platform provides intuitive ways to interact with your data.

To load data from a CSV file named data.csv, you can use:

load data from "data.csv" as my_data

Once loaded, you can perform operations on my_data. For instance, to select specific columns:

select column1, column2 from my_data as processed_data

Rendering Output

After processing your data, you'll likely want to visualize or export it. We support several output formats.

To render the processed_data as a JSON file:

render processed_data to "output.json" format json

Or, to render it as a table in your console:

render processed_data to console format table

Advanced Usage

This section covers more complex features such as scripting, custom modules, and API integrations. Dive deeper into the advanced usage guide for more information.

Troubleshooting

Encountering issues? Our troubleshooting guide provides solutions to common problems. You can also find help in our community forum.