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  1. Stackups
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  5. Cube.js vs GraphQL

Cube.js vs GraphQL

OverviewComparisonAlternatives

Overview

GraphQL
GraphQL
Stacks34.9K
Followers28.1K
Votes309
Cube
Cube
Stacks96
Followers258
Votes30

Cube.js vs GraphQL: What are the differences?

Introduction

Cube.js is an open-source analytical API platform that allows developers to create and deploy data APIs quickly. On the other hand, GraphQL is a query language and runtime for APIs that enables clients to request only the data they need.

  1. Data Modeling: Cube.js provides a powerful data modeling layer that facilitates defining complex relationships between tables, dimensions, and measures. It offers a schema definition language and supports multiple databases, making it easier to work with diverse data sources. In contrast, GraphQL does not provide a built-in data modeling layer and requires developers to define the schema manually.

  2. Aggregation and Analytics: Cube.js focuses on providing analytics capabilities out of the box. It supports pre-aggregations, caching, and fetching data in a multi-level hierarchical manner, which enables it to handle complex analytical queries with great performance. GraphQL, although flexible, does not have built-in features for aggregation and analytics. It primarily serves as a query language for fetching data from various sources.

  3. Real-time Capabilities: Cube.js is designed to handle real-time data processing and allows developers to easily build real-time dashboards and applications. It supports WebSocket-based live queries, event-based data updates, and real-time streaming of data changes. GraphQL, on the other hand, does not have built-in mechanisms for real-time data processing. It requires additional tools or libraries to implement real-time capabilities.

  4. Schema Stitching and Federation: Cube.js does not provide native support for schema stitching and federation. It primarily focuses on providing a unified API layer for analytics and reporting use cases. GraphQL, on the other hand, was designed to support schema stitching and federation out of the box. It allows developers to combine multiple GraphQL schemas into a single federated schema, enabling composition of APIs from different sources.

  5. Out-of-the-box Integrations: Cube.js offers seamless integrations with popular business intelligence tools like Tableau, Power BI, and Looker. It provides dedicated connectors to these tools, making it easier to consume analytical data in a variety of ways. GraphQL, on the other hand, does not have dedicated integrations with specific BI tools. It primarily focuses on exposing a flexible API layer for data fetching and manipulation.

  6. Community and Ecosystem: Cube.js has a growing community of developers and a vibrant ecosystem of extensions and plugins. It benefits from the active contributions and support from the community, which accelerates its development and adoption. GraphQL, being an open standard, has a larger community and a vast ecosystem of tools, libraries, and frameworks. It has gained significant traction and is widely adopted by organizations across different industries.

In Summary, Cube.js provides a powerful data modeling layer, built-in analytics capabilities, and real-time processing support. It has seamless integrations with popular BI tools and benefits from an active community. On the other hand, GraphQL offers a flexible query language for fetching data from various sources, supports schema stitching and federation, and has a larger community and ecosystem.

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Detailed Comparison

GraphQL
GraphQL
Cube
Cube

GraphQL is a data query language and runtime designed and used at Facebook to request and deliver data to mobile and web apps since 2012.

Cube: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.

Hierarchical;Product-centric;Client-specified queries;Backwards Compatible;Structured, Arbitrary Code;Application-Layer Protocol;Strongly-typed;Introspective
* Pre-aggregation; * Caching; * Data modeling; * APIs; * Works with any relational database;
Statistics
Stacks
34.9K
Stacks
96
Followers
28.1K
Followers
258
Votes
309
Votes
30
Pros & Cons
Pros
  • 75
    Schemas defined by the requests made by the user
  • 63
    Will replace RESTful interfaces
  • 62
    The future of API's
  • 49
    The future of databases
  • 12
    Self-documenting
Cons
  • 4
    Hard to migrate from GraphQL to another technology
  • 4
    More code to type.
  • 2
    Takes longer to build compared to schemaless.
  • 1
    All the pros sound like NFT pitches
  • 1
    No support for caching
Pros
  • 8
    API
  • 6
    Visualization agnostic
  • 6
    Caching
  • 6
    Open Source
  • 4
    Rollups orchestration
Cons
  • 1
    Poor performance
  • 1
    Cannot use as a lib - only HTTP
  • 1
    No ability to update "cubes" in runtime
  • 1
    Doesn't support filtering on left joins
  • 1
    Incomplete documentation
Integrations
No integrations available
Amazon Redshift
Amazon Redshift
Google BigQuery
Google BigQuery
Microsoft SQL Server
Microsoft SQL Server
Snowflake
Snowflake
Presto
Presto
MySQL
MySQL
PostgreSQL
PostgreSQL
Microsoft Azure
Microsoft Azure
Oracle
Oracle
Amazon Athena
Amazon Athena

What are some alternatives to GraphQL, Cube?

Metabase

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Prisma

Prisma

Prisma is an open-source database toolkit. It replaces traditional ORMs and makes database access easy with an auto-generated query builder for TypeScript & Node.js.

PostGraphile

PostGraphile

Execute one command (or mount one Node.js middleware) and get an instant high-performance GraphQL API for your PostgreSQL database

Superset

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

OData

OData

It is an ISO/IEC approved, OASIS standard that defines a set of best practices for building and consuming RESTful APIs. It helps you focus on your business logic while building RESTful APIs without having to worry about the various approaches to define request and response headers, status codes, HTTP methods, URL conventions, media types, payload formats, query options, etc.

Power BI

Power BI

It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.

Mode

Mode

Created by analysts, for analysts, Mode is a SQL-based analytics tool that connects directly to your database. Mode is designed to alleviate the bottlenecks in today's analytical workflow and drive collaboration around data projects.

Google Datastudio

Google Datastudio

It lets you create reports and data visualizations. Data Sources are reusable components that connect a report to your data, such as Google Analytics, Google Sheets, Google AdWords and so forth. You can unlock the power of your data with interactive dashboards and engaging reports that inspire smarter business decisions.

AskNed

AskNed

AskNed is an analytics platform where enterprise users can get answers from their data by simply typing questions in plain English.

Shiny

Shiny

It is an open source R package that provides an elegant and powerful web framework for building web applications using R. It helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge.

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