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Cube.js Introduction

Cube.js is an open source modular framework to build analytical web applications. It is primarily used to build internal business intelligence tools or to add customer-facing analytics to an existing application.

Cube.js was designed to work with Serverless Query Engines like AWS Athena and Google BigQuery. Multi-stage querying approach makes it suitable for handling trillions of data points. Most modern RDBMS work with Cube.js as well and can be tuned for adequate performance.

Unlike others, it is not a monolith application, but a set of modules, which does one thing well. Cube.js provides modules to run transformations and modeling in data warehouse, querying and caching, managing API gateway and building UI on top of that.

  • Cube.js Schema. It acts as an ORM for analytics and allows to model everything from simple counts to cohort retention and funnel analysis.
  • Cube.js Query Orchestration and Cache. It optimizes query execution by breaking queries into small, fast, reusable and materialzed pieces.
  • Cube.js API Gateway. It provides idempotent long polling API which guarantees analytic query results delivery without request time frame limitations and tolerant to connectivity issues.

  • Cube.js Javascript Client. It provides idempotent long polling API which guarantees analytic query results delivery without request time frame limitations and tolerant to connectivity issues.
  • Cube.js React. React wrapper for Cube.js API.

If you are building your own business intelligence tool or customer-facing analytics most probably you'll face following problems:

  1. Performance. Most of effort time in modern analytics software development is spent to provide adequate time to insight. In the world where every company data is a big data writing just SQL query to get insight isn't enough anymore.
  2. SQL code organization. Modelling even a dozen of metrics with a dozen of dimensions using pure SQL queries sooner or later becomes a maintenance nightmare which ends up in building modelling framework.
  3. Infrastructure. Key components every production-ready analytics solution requires: analytic SQL generation, query results caching and execution orchestration, data pre-aggregation, security, API for query results fetch, and visualization.

Cube.js has necessary infrastructure for every analytic application that heavily relies on its caching and pre-aggregation layer to provide several minutes raw data to insight delay and sub second API response times on a trillion of data points scale.

Cube.js acts as an analytics backend, translating business logic (metrics and dimensions) into SQL and handling database connection.

The Cube.js javascript Client performs queries, expressed via dimensions, measures, and filters. The Server uses Cube.js Schema to generate a SQL code, which is executed by your database. The Server handles all the database connection, as well as pre-aggregations and caching layers. The result then sent back to the Client. The Client itself is visualization agnostic and works well with any chart library.

Cube.js