chartbrew

Open-source web platform used to create live reporting dashboards from APIs, MongoDB, Firestore, MySQL, PostgreSQL, and more 📈📊

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Quick Overview

Chartbrew is an open-source web application that allows users to create and share interactive charts and dashboards. It connects to various data sources, including databases and APIs, enabling users to visualize and analyze their data without writing complex queries or code.

Pros

User-friendly interface for creating charts and dashboards
Supports multiple data sources, including PostgreSQL, MySQL, MongoDB, and various APIs
Customizable charts with a wide range of visualization options
Collaboration features for team-based dashboard creation and sharing

Cons

Limited advanced analytics capabilities compared to enterprise-level BI tools
Requires self-hosting, which may be challenging for non-technical users
Documentation could be more comprehensive for some advanced features
Community support is still growing, potentially leading to slower issue resolution

Getting Started

To get started with Chartbrew, follow these steps:

Clone the repository:

git clone https://github.com/chartbrew/chartbrew.git

Navigate to the project directory:
```
cd chartbrew
```
Install dependencies:
```
npm run setup
```
Configure the environment variables in the .env file
Start the development server:
```
npm run dev
```
Open your browser and visit http://localhost:4018 to access Chartbrew

For more detailed instructions and configuration options, refer to the project's documentation on GitHub.

Competitor Comparisons

metabase

39,673

The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:

Pros of Metabase

More mature and feature-rich, with a larger community and extensive documentation
Supports a wider range of data sources and advanced analytics capabilities
Offers both cloud-hosted and self-hosted options for deployment

Cons of Metabase

Steeper learning curve due to its extensive feature set
Requires more resources to run, potentially making it less suitable for smaller projects
Less flexible for custom integrations compared to Chartbrew's API-first approach

Code Comparison

Metabase (Java):

public class Query {
    private final String query;
    private final Map<String, Object> parameters;

    public Query(String query, Map<String, Object> parameters) {
        this.query = query;
        this.parameters = parameters;
    }
}

Chartbrew (JavaScript):

const Chart = require("../models/Chart");

exports.createChart = async (req, res) => {
  try {
    const chart = await Chart.create(req.body);
    return res.status(200).json(chart);
  } catch (error) {
    return res.status(400).json(error);
  }
};

Both repositories offer data visualization and analytics capabilities, but they cater to different use cases. Metabase is a more comprehensive solution suitable for larger organizations with diverse data needs, while Chartbrew provides a lightweight, API-driven approach that may be more appealing to developers and smaller teams looking for quick integration and customization.

superset

63,878

Apache Superset is a Data Visualization and Data Exploration Platform

Pros of Superset

More extensive feature set and wider range of visualization options
Larger community and ecosystem, with better long-term support
Advanced security features and role-based access control

Cons of Superset

Steeper learning curve and more complex setup process
Heavier resource requirements, potentially less suitable for smaller projects
Less focus on real-time data updates compared to Chartbrew

Code Comparison

Superset (Python):

from superset import db
from superset.models.slice import Slice

slice = Slice(
    slice_name="My Chart",
    datasource_type="table",
    datasource_id=1,
    viz_type="bar",
    params="{}"
)
db.session.add(slice)
db.session.commit()

Chartbrew (JavaScript):

const Chart = require("../models/Chart");

const newChart = await Chart.create({
  name: "My Chart",
  type: "bar",
  datasetId: 1,
  chartData: {},
});

Both Superset and Chartbrew are data visualization and business intelligence tools, but they cater to different scales and use cases. Superset offers a more comprehensive solution for large-scale enterprise needs, while Chartbrew provides a simpler, more lightweight option for smaller projects or teams looking for quick setup and real-time data visualization capabilities.

grafana

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Pros of Grafana

More mature and feature-rich platform with extensive plugin ecosystem
Supports a wider range of data sources and visualization types
Robust alerting and notification system

Cons of Grafana

Steeper learning curve, especially for non-technical users
Can be resource-intensive for large-scale deployments
More complex setup and configuration process

Code Comparison

Grafana (JavaScript):

import { PanelPlugin } from '@grafana/data';
import { SimplePanel } from './SimplePanel';

export const plugin = new PanelPlugin<SimpleOptions>(SimplePanel).setPanelOptions(builder => {
  return builder.addTextInput({
    path: 'text',
    name: 'Simple text option',
    description: 'Description of panel option',
    defaultValue: 'Default value of text input option',
  });
});

Chartbrew (JavaScript):

import React from "react";
import { Chart } from "@chartbrew/client";

const MyChart = () => {
  return <Chart projectId="your-project-id" chartId="your-chart-id" />;
};

export default MyChart;

Both repositories offer data visualization solutions, but Grafana provides a more comprehensive and customizable platform suitable for complex monitoring needs. Chartbrew, on the other hand, offers a simpler, more user-friendly approach to creating charts and dashboards, making it more accessible for beginners or those seeking a quick setup.

redash

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Pros of Redash

More mature project with a larger community and extensive documentation
Supports a wider range of data sources out-of-the-box
Offers advanced features like query scheduling and alerts

Cons of Redash

Steeper learning curve for non-technical users
Requires more server resources to run effectively
Less focus on modern, interactive visualizations

Code Comparison

Redash (Python):

from redash import models

query = models.Query.create(
    data_source=data_source,
    query_text="SELECT * FROM users",
    name="All Users",
    user=user,
    schedule={"interval": 3600}
)

Chartbrew (JavaScript):

const chart = new Chart({
  name: "User Growth",
  type: "line",
  datasets: [{
    data: [12, 19, 3, 5, 2, 3]
  }]
});

Both Redash and Chartbrew are data visualization and business intelligence tools, but they cater to different user needs. Redash is more powerful and flexible, suitable for data analysts and engineers who need to work with complex queries and diverse data sources. Chartbrew, on the other hand, offers a more user-friendly interface and focuses on creating visually appealing charts and dashboards with less technical complexity.

cube

18,386

📊 Cube — Universal semantic layer platform for AI, BI, spreadsheets, and embedded analytics

Pros of Cube

More extensive and flexible data modeling capabilities
Supports a wider range of databases and data sources
Better suited for large-scale, enterprise-level analytics projects

Cons of Cube

Steeper learning curve and more complex setup process
Requires more technical expertise to implement and maintain
Less focus on out-of-the-box visualizations compared to Chartbrew

Code Comparison

Cube query example:

cube(`Orders`, {
  sql: `SELECT * FROM orders`,
  measures: {
    count: {
      type: `count`,
    },
  },
  dimensions: {
    status: {
      sql: `status`,
      type: `string`,
    },
  },
});

Chartbrew query example:

{
  "type": "mysql",
  "query": "SELECT * FROM orders",
  "fields": [
    { "name": "status", "type": "string" },
    { "name": "count", "type": "number", "aggregation": "count" }
  ]
}

Both Cube and Chartbrew are data analytics and visualization tools, but they cater to different use cases and skill levels. Cube is more powerful and flexible, suitable for complex data modeling and large-scale projects. It offers extensive customization options but requires more technical expertise. Chartbrew, on the other hand, provides a more user-friendly interface and focuses on quick, easy-to-setup visualizations, making it more accessible for smaller teams or projects with simpler requirements.

lightdash

4,560

Self-serve BI to 10x your data team ⚡️

Pros of Lightdash

Built specifically for dbt users, offering seamless integration with dbt projects
Supports version control for metrics and dimensions, enhancing collaboration
Provides a more robust SQL editor with autocompletion and error highlighting

Cons of Lightdash

Limited to dbt-compatible data warehouses, reducing flexibility compared to Chartbrew
Steeper learning curve for users not familiar with dbt or SQL
Less extensive range of chart types and visualizations than Chartbrew

Code Comparison

Lightdash SQL query example:

SELECT
  order_date,
  SUM(total_amount) AS total_sales
FROM ${ref('orders')}
GROUP BY order_date
ORDER BY order_date

Chartbrew chart configuration example:

{
  type: "line",
  datasets: [
    {
      datasetId: "sales_data",
      xAxis: "order_date",
      yAxis: "total_amount"
    }
  ]
}

Both repositories focus on data visualization and analytics, but they cater to different user needs. Lightdash is tailored for dbt users and offers more advanced SQL capabilities, while Chartbrew provides a more user-friendly interface and broader data source compatibility. The choice between the two depends on the user's technical expertise and specific project requirements.

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README

Chartbrew is an open-source web application that can connect directly to databases and APIs and use the data to create beautiful charts. It features a chart builder, editable dashboards, embedable charts, query & requests editor, and team capabilities.

Chartbrew as a service is available here

ð Read the full docs here

ð¡ Have any ideas or discussion topics?

ð¬ Join our Discord

Data sources

Check Chartbrew's website for the latest list of supported data sources

Prerequisites

NodeJS v20
MySQL (5+) or PostgreSQL (12.5+)
Redis (v6+)

Start

It is recommended you head over to the more detailed documentation to find out how to set up Chartbrew

ð You can find it here

Set up Chartbrew locally

Create a new database

Chartbrew can run on MySQL or PostgreSQL. Create an empty database that Chartbrew can use.

Clone and setup

git clone https://github.com/chartbrew/chartbrew.git
cd chartbrew && npm run setup

Complete the required environmental variables in chartbrew/.env. Check out which need to be set here.

Run the project in Development

Open two terminals, one for front-end and the other for back-end.

# frontend
cd client/
npm run start

# backend
cd server/
npm run start-dev

Head over to http://localhost:4018 to see the app running and create your first user account.

Deploy Chartbrew on Render

Deploy Chartbrew on Heroku and Vercel

Run with Docker

Check the full guide in the docs.

Quickstart

A Chartbrew docker image is built whenever a new version is released.

Before running the commands below, make sure you have a MySQL server already running and an empty database that Chartbrew can use. The database name should match the value of the CB_DB_NAME variable.

You will need a 32 bytes AES encryption key for the CB_ENCRYPTION_KEY variable. Run the following command to generate one:

node -e "console.log(require('crypto').randomBytes(32).toString('hex'))"

docker pull razvanilin/chartbrew

docker run -p 4019:4019 -p 4018:4018 \
  -e CB_ENCRYPTION_KEY=your_32_bytes_key \
  -e CB_API_HOST=0.0.0.0 \
  -e CB_API_PORT=4019 \
  -e CB_DB_HOST=host.docker.internal \
  -e CB_DB_PORT=3306 \
  -e CB_DB_NAME=chartbrew \
  -e CB_DB_USERNAME=root \
  -e CB_DB_PASSWORD=password \
  -e CB_REDIS_HOST=host.docker.internal \
  -e CB_REDIS_PORT=6379 \
  -e CB_REDIS_PASSWORD=password \
  -e VITE_APP_CLIENT_HOST=http://localhost:4018 \
  -e VITE_APP_CLIENT_PORT=4018 \
  -e VITE_APP_API_HOST=http://localhost:4019 \
  razvanilin/chartbrew

Acknowledgements

Many thanks to everybody that contributed to this open-source project ð

Start here if you want to become a contributor

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