cube

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

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Quick Overview

Cube.js is an open-source analytics API platform designed to build internal business intelligence tools or add customer-facing analytics to existing applications. It provides a robust framework for data modeling, caching, and API generation, allowing developers to create scalable and performant analytics solutions.

Pros

Flexible data modeling with support for various data sources
Automatic caching and query optimization for improved performance
Pre-aggregations for handling large datasets efficiently
Extensive API support for integration with various frontend frameworks

Cons

Steep learning curve for beginners
Limited visualization capabilities out of the box
Requires additional setup for authentication and authorization
May be overkill for simple analytics needs

Code Examples

Defining a cube (data model):

cube(`Orders`, {
  sql: `SELECT * FROM orders`,
  
  measures: {
    count: {
      type: `count`
    },
    totalAmount: {
      sql: `amount`,
      type: `sum`
    }
  },
  
  dimensions: {
    status: {
      sql: `status`,
      type: `string`
    },
    createdAt: {
      sql: `created_at`,
      type: `time`
    }
  }
});

Querying data using Cube.js API:

const cubejsApi = cubejs(
  'CUBEJS-API-TOKEN',
  { apiUrl: 'http://localhost:4000/cubejs-api/v1' }
);

const query = {
  measures: ['Orders.count', 'Orders.totalAmount'],
  timeDimensions: [{
    dimension: 'Orders.createdAt',
    dateRange: ['2021-01-01', '2021-12-31'],
    granularity: 'month'
  }],
  filters: [{
    member: 'Orders.status',
    operator: 'equals',
    values: ['completed']
  }]
};

cubejsApi.load(query).then(resultSet => {
  console.log(resultSet.chartPivot());
});

Setting up pre-aggregations:

cube(`Orders`, {
  // ... other cube definitions

  preAggregations: {
    main: {
      measures: [Orders.count, Orders.totalAmount],
      dimensions: [Orders.status],
      timeDimension: Orders.createdAt,
      granularity: 'month'
    }
  }
});

Getting Started

Install Cube.js CLI:
```
npm install -g cubejs-cli
```

Create a new Cube.js project:

cubejs create my-analytics-app -d postgres

Navigate to the project directory and start the development server:
```
cd my-analytics-app
npm run dev
```
Open http://localhost:4000 in your browser to access the Cube.js Developer Playground and start building your analytics application.

Competitor Comparisons

superset

65,934

Apache Superset is a Data Visualization and Data Exploration Platform

Pros of Superset

More comprehensive visualization capabilities with a wide range of chart types
Built-in SQL Lab for direct querying and exploration of data sources
Larger community and ecosystem, being an Apache project

Cons of Superset

Steeper learning curve due to its extensive features and configurations
Less flexible for custom data modeling compared to Cube's semantic layer
May require more server resources for deployment and operation

Code Comparison

Superset (Python):

from superset import db
from superset.models import SqlaTable

table = SqlaTable(
    table_name='my_table',
    schema='my_schema',
    database_id=1,
    sql='SELECT * FROM my_table'
)
db.session.add(table)
db.session.commit()

Cube (JavaScript):

cube(`MyTable`, {
  sql: `SELECT * FROM my_schema.my_table`,
  measures: {
    count: {
      type: `count`
    }
  },
  dimensions: {
    id: {
      sql: `id`,
      type: `number`,
      primaryKey: true
    }
  }
});

Both Superset and Cube offer powerful data analytics and visualization capabilities, but they cater to different use cases. Superset excels in providing a comprehensive BI platform with rich visualizations, while Cube focuses on building a flexible semantic layer for data modeling and API generation.

metabase

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Pros of Metabase

User-friendly interface with drag-and-drop query builder
Built-in visualization tools and dashboard creation
Supports multiple databases out of the box

Cons of Metabase

Less flexible for complex data modeling scenarios
Limited customization options for advanced users
Steeper learning curve for non-technical users

Code Comparison

Metabase (SQL query):

SELECT product_name, SUM(sales_amount) AS total_sales
FROM sales
GROUP BY product_name
ORDER BY total_sales DESC
LIMIT 10

Cube (data schema):

cube(`Sales`, {
  sql: `SELECT * FROM sales`,
  measures: {
    totalSales: {
      sql: `sales_amount`,
      type: `sum`
    }
  },
  dimensions: {
    productName: {
      sql: `product_name`,
      type: `string`
    }
  }
});

Both Metabase and Cube offer solutions for data analytics and business intelligence, but they approach the problem differently. Metabase provides a more complete out-of-the-box solution with a user-friendly interface, while Cube focuses on providing a flexible data modeling layer that can be integrated with various frontend tools. The code comparison shows how Metabase uses SQL queries directly, while Cube uses a JavaScript-based schema definition to model data and create reusable metrics and dimensions.

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 a user-friendly interface for non-technical users to create visualizations

Cons of Redash

Less flexible for complex data modeling scenarios
Limited customization options for advanced users
Slower query performance for large datasets compared to Cube's pre-aggregations

Code Comparison

Redash query example:

SELECT date_trunc('day', created_at) AS day, COUNT(*) AS count
FROM events
WHERE created_at > current_date - interval '7 days'
GROUP BY 1
ORDER BY 1

Cube query example:

cube(`Events`, {
  sql: `SELECT * FROM events`,
  measures: {
    count: {
      type: `count`
    }
  },
  dimensions: {
    createdAt: {
      sql: `created_at`,
      type: `time`
    }
  }
});

Both projects aim to simplify data analysis and visualization, but they take different approaches. Redash focuses on providing a complete solution with a user-friendly interface, while Cube offers more flexibility and performance optimization for developers building data applications.

echarts

63,369

Apache ECharts is a powerful, interactive charting and data visualization library for browser

Pros of ECharts

Extensive charting library with a wide variety of chart types and customization options
Lightweight and high-performance rendering, suitable for large datasets
Strong community support and regular updates

Cons of ECharts

Steeper learning curve due to its extensive API and configuration options
Primarily focused on data visualization, lacking built-in data processing capabilities
May require additional libraries for complex data transformations

Code Comparison

ECharts:

option = {
  xAxis: {
    type: 'category',
    data: ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
  },
  yAxis: {
    type: 'value'
  },
  series: [{
    data: [120, 200, 150, 80, 70, 110, 130],
    type: 'bar'
  }]
};

Cube:

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

While ECharts focuses on rendering charts with predefined data, Cube is designed for data modeling and analytics, providing a foundation for building data applications. ECharts excels in visualization flexibility, while Cube offers robust data processing capabilities.

grafana

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Pros of Grafana

More mature and widely adopted visualization platform
Supports a broader range of data sources out-of-the-box
Offers a rich set of pre-built dashboards and plugins

Cons of Grafana

Steeper learning curve for complex visualizations
Less flexibility in data modeling and transformation
Limited support for real-time analytics without additional tools

Code Comparison

Grafana (Dashboard JSON):

{
  "panels": [
    {
      "type": "graph",
      "title": "CPU Usage",
      "datasource": "Prometheus",
      "targets": [
        { "expr": "node_cpu_usage" }
      ]
    }
  ]
}

Cube (Data Schema):

cube(`CPUUsage`, {
  sql: `SELECT * FROM node_cpu_usage`,
  measures: {
    average: {
      type: `avg`,
      sql: `usage_percent`
    }
  },
  dimensions: {
    timestamp: {
      type: `time`,
      sql: `timestamp`
    }
  }
});

While Grafana focuses on visualization configuration, Cube emphasizes data modeling and schema definition. Grafana's approach is more suitable for quick dashboard creation, while Cube provides greater flexibility in data preparation and analysis.

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README

Website â¢ Getting Started â¢ Docs â¢ Examples â¢ Blog â¢ Slack â¢ Twitter

Cube is the semantic layer for building data applications. It helps data engineers and application developers access data from modern data stores, organize it into consistent definitions, and deliver it to every application.

Learn more about connecting Cube to data sources and analytics & visualization tools.

Cube was designed to work with all SQL-enabled data sources, including cloud data warehouses like Snowflake or Google BigQuery, query engines like Presto or Amazon Athena, and application databases like Postgres. Cube has a built-in relational caching engine to provide sub-second latency and high concurrency for API requests.

For more details, see the introduction page in our documentation.

Why Cube?

If you are building a data applicationâsuch as a business intelligence tool or a customer-facing analytics featureâyouâll probably face the following problems:

SQL code organization. Sooner or later, modeling even a dozen metrics with a dozen dimensions using pure SQL queries becomes a maintenance nightmare, which leads to building a modeling framework.
Performance. Most of the time and effort in modern analytics software development is spent providing adequate time to insight. In a world where every companyâs data is big data, writing just SQL queries to get insight isnât enough anymore.
Access Control. It is important to secure and govern access to data for all downstream data consuming applications.

Cube has the necessary infrastructure and features to implement efficient data modeling, access control, and performance optimizations so that every applicationâlike embedded analytics, dashboarding and reporting tools, data notebooks, and other toolsâcan access consistent data via REST, SQL, and GraphQL APIs.

Getting Started ð

Cube Cloud

Cube Cloud is the fastest way to get started with Cube. It provides managed infrastructure as well as an instant and free access for development projects and proofs of concept.

For a step-by-step guide on Cube Cloud, see the docs.

Docker

Alternatively, you can get started with Cube locally or self-host it with Docker.

Once Docker is installed, in a new folder for your project, run the following command:

docker run -p 4000:4000 \
  -p 15432:15432 \
  -v ${PWD}:/cube/conf \
  -e CUBEJS_DEV_MODE=true \
  cubejs/cube

Then, open http://localhost:4000 in your browser to continue setup.

For a step-by-step guide on Docker, see the docs.

Resources

Contributing

There are many ways you can contribute to Cube! Here are a few possibilities:

Star this repo and follow us on Twitter.
Add Cube to your stack on Stackshare.
Upvote issues with ð reaction so we know what's the demand for particular issue to prioritize it within road map.
Create issues every time you feel something is missing or goes wrong.
Ask questions on Stack Overflow with cube.js tag if others can have these questions as well.
Provide pull requests for all open issues and especially for those with help wanted and good first issue labels.

All sort of contributions are welcome and extremely helpful ð Please refer to the contribution guide for more information.

License

Cube Client is MIT licensed.

Cube Backend is Apache 2.0 licensed.

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Quick Overview

Pros

Cons

Code Examples

Getting Started

Competitor Comparisons

Pros of Superset

Cons of Superset

Code Comparison

Pros of Metabase

Cons of Metabase

Code Comparison

Pros of Redash

Cons of Redash

Code Comparison

Pros of ECharts

Cons of ECharts

Code Comparison

Pros of Grafana

Cons of Grafana

Code Comparison

Convert designs to code with AI

README

Why Cube?

Getting Started ð

Cube Cloud

Docker

Resources

Contributing

License

Top Related Projects

Convert designs to code with AI

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Getting Started ð