Alternatives to Cube.js logo

Alternatives to Cube.js

GraphQL, Metabase, Tableau, Power BI, and Looker are the most popular alternatives and competitors to Cube.js.
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What is Cube.js and what are its top alternatives?

Cube.js is an analytics layer for modern applications. It supplies building blocks to add analytics features into any application you create. It is designed to work with large-scale data sets and implements various optimization techniques.
Cube.js is a tool in the Business Intelligence category of a tech stack.
Cube.js is an open source tool with 11.8K GitHub stars and 1.1K GitHub forks. Here’s a link to Cube.js's open source repository on GitHub

Top Alternatives to Cube.js

  • GraphQL

    GraphQL

    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. ...

  • 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. ...

  • Tableau

    Tableau

    Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click. ...

  • 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. ...

  • Looker

    Looker

    We've built a unique data modeling language, connections to today's fastest analytical databases, and a service that you can deploy on any infrastructure, and explore on any device. Plus, we'll help you every step of the way. ...

  • Data Studio

    Data Studio

    Unlock the power of your data with interactive dashboards and engaging reports that inspire smarter business decisions. It’s easy and free. ...

  • Redash

    Redash

    Redash helps you make sense of your data. Connect and query your data sources, build dashboards to visualize data and share them with your company. ...

  • 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. ...

Cube.js alternatives & related posts

GraphQL logo

GraphQL

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A data query language and runtime
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PROS OF GRAPHQL
  • 69
    Schemas defined by the requests made by the user
  • 62
    Will replace RESTful interfaces
  • 59
    The future of API's
  • 47
    The future of databases
  • 12
    Self-documenting
  • 11
    Get many resources in a single request
  • 5
    Ask for what you need, get exactly that
  • 4
    Query Language
  • 3
    Evolve your API without versions
  • 3
    Fetch different resources in one request
  • 3
    Type system
  • 2
    GraphiQL
  • 2
    Ease of client creation
  • 2
    Easy setup
  • 1
    Good for apps that query at build time. (SSR/Gatsby)
  • 1
    Backed by Facebook
  • 1
    Easy to learn
  • 1
    "Open" document
  • 1
    Better versioning
  • 1
    Standard
  • 1
    1. Describe your data
  • 1
    Fast prototyping
CONS OF GRAPHQL
  • 3
    Hard to migrate from GraphQL to another technology
  • 3
    More code to type.
  • 1
    Works just like any other API at runtime
  • 1
    Takes longer to build compared to schemaless.

related GraphQL posts

Shared insights
on
Node.jsNode.jsGraphQLGraphQLMongoDBMongoDB

I just finished the very first version of my new hobby project: #MovieGeeks. It is a minimalist online movie catalog for you to save the movies you want to see and for rating the movies you already saw. This is just the beginning as I am planning to add more features on the lines of sharing and discovery

For the #BackEnd I decided to use Node.js , GraphQL and MongoDB:

  1. Node.js has a huge community so it will always be a safe choice in terms of libraries and finding solutions to problems you may have

  2. GraphQL because I needed to improve my skills with it and because I was never comfortable with the usual REST approach. I believe GraphQL is a better option as it feels more natural to write apis, it improves the development velocity, by definition it fixes the over-fetching and under-fetching problem that is so common on REST apis, and on top of that, the community is getting bigger and bigger.

  3. MongoDB was my choice for the database as I already have a lot of experience working on it and because, despite of some bad reputation it has acquired in the last months, I still believe it is a powerful database for at least a very long list of use cases such as the one I needed for my website

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Nick Rockwell
SVP, Engineering at Fastly · | 44 upvotes · 1.7M views

When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

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Metabase logo

Metabase

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An open-source business intelligence tool
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PROS OF METABASE
  • 54
    Database visualisation
  • 41
    Open Source
  • 39
    Easy setup
  • 33
    Dashboard out of the box
  • 18
    Free
  • 12
    Simple
  • 8
    Support for many dbs
  • 7
    Easy embedding
  • 6
    It's good
  • 6
    Easy
  • 5
    AGPL : wont help with adoption but depends on your goal
  • 5
    BI doesn't get easier than that
  • 4
    Multiple integrations
  • 4
    Google analytics integration
  • 3
    Easy set up
CONS OF METABASE
  • 3
    Harder to setup than similar tools

related Metabase posts

Tableau logo

Tableau

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Tableau helps people see and understand data.
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PROS OF TABLEAU
  • 3
    Capable of visualising billions of rows
  • 1
    Responsive
CONS OF TABLEAU
    Be the first to leave a con

    related Tableau posts

    Looking for the best analytics software for a medium-large-sized firm. We currently use a Microsoft SQL Server database that is analyzed in Tableau desktop/published to Tableau online for users to access dashboards. Is it worth the cost savings/time to switch over to using SSRS or Power BI? Does anyone have experience migrating from Tableau to SSRS /or Power BI? Our other option is to consider using Tableau on-premises instead of online. Using custom SQL with over 3 million rows really decreases performances and results in processing times that greatly exceed our typical experience. Thanks.

    See more
    Power BI logo

    Power BI

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    Empower team members to discover insights hidden in your data
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    PROS OF POWER BI
    • 7
      Cross-filtering
    CONS OF POWER BI
      Be the first to leave a con

      related Power BI posts

      Looking for the best analytics software for a medium-large-sized firm. We currently use a Microsoft SQL Server database that is analyzed in Tableau desktop/published to Tableau online for users to access dashboards. Is it worth the cost savings/time to switch over to using SSRS or Power BI? Does anyone have experience migrating from Tableau to SSRS /or Power BI? Our other option is to consider using Tableau on-premises instead of online. Using custom SQL with over 3 million rows really decreases performances and results in processing times that greatly exceed our typical experience. Thanks.

      See more
      Looker logo

      Looker

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      Pioneering the next generation of BI, data discovery & data analytics
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      PROS OF LOOKER
      • 4
        Real time in app customer chat support
      • 4
        GitHub integration
      • 1
        Reduces the barrier of entry to utilizing data
      CONS OF LOOKER
      • 1
        Price

      related Looker posts

      Mohan Ramanujam

      We are a consumer mobile app IOS/Android startup. The app is instrumented with branch and Firebase. We use Google BigQuery. We are looking at tools that can support engagement and cohort analysis at an early stage price which we can grow with. Data Studio is the default but it would seem Looker provides more power. We don't have much insight into Amplitude other than the fact it is a popular PM tool. Please provide some insight.

      See more
      Data Studio logo

      Data Studio

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      Your data is powerful. Use it
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      PROS OF DATA STUDIO
        Be the first to leave a pro
        CONS OF DATA STUDIO
          Be the first to leave a con

          related Data Studio posts

          Mohan Ramanujam

          We are a consumer mobile app IOS/Android startup. The app is instrumented with branch and Firebase. We use Google BigQuery. We are looking at tools that can support engagement and cohort analysis at an early stage price which we can grow with. Data Studio is the default but it would seem Looker provides more power. We don't have much insight into Amplitude other than the fact it is a popular PM tool. Please provide some insight.

          See more
          Redash logo

          Redash

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          Easily query an existing database, share the dataset and visualize it in different ways
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          PROS OF REDASH
          • 5
            Open Source
          • 3
            SQL Friendly
          CONS OF REDASH
          • 1
            All results are loaded into RAM before displaying
          • 1
            Memory Leaks

          related Redash posts

          Superset logo

          Superset

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          Data exploration and visualization platform, by Airbnb
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          PROS OF SUPERSET
          • 9
            Awesome interactive filtering
          • 6
            Wide SQL database support
          • 5
            Free
          • 5
            Shareable & editable dashboards
          • 3
            Easy to share charts & dasboards
          • 2
            User & Role Management
          • 2
            Great for data collaborating on data exploration
          CONS OF SUPERSET
          • 3
            Ugly GUI
          • 2
            It is difficult to install on the server
          • 2
            Link diff db together "Data Modeling "

          related Superset posts

          Julien DeFrance
          Principal Software Engineer at Tophatter · | 16 upvotes · 2.4M views

          Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

          I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

          For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

          Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

          Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

          Future improvements / technology decisions included:

          Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

          As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

          One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

          See more