Alternatives to Data Studio logo

Alternatives to Data Studio

Metabase, Amazon Quicksight, Klipfolio, Google Analytics, and AzureDataStudio are the most popular alternatives and competitors to Data Studio.
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What is Data Studio and what are its top alternatives?

Unlock the power of your data with interactive dashboards and engaging reports that inspire smarter business decisions. It鈥檚 easy and free.
Data Studio is a tool in the Business Intelligence category of a tech stack.

Data Studio alternatives & related posts

Amazon Quicksight logo

Amazon Quicksight

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44
1
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Fast, easy to use business analytics at 1/10th the cost of traditional BI solutions
Amazon Quicksight logo
Amazon Quicksight
VS
Data Studio logo
Data Studio

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Julien DeFrance
Julien DeFrance
Principal Software Engineer at Tophatter | 16 upvotes 400.6K views
atSmartZipSmartZip
Amazon DynamoDB
Amazon DynamoDB
Ruby
Ruby
Node.js
Node.js
AWS Lambda
AWS Lambda
New Relic
New Relic
Amazon Elasticsearch Service
Amazon Elasticsearch Service
Elasticsearch
Elasticsearch
Superset
Superset
Amazon Quicksight
Amazon Quicksight
Amazon Redshift
Amazon Redshift
Zapier
Zapier
Segment
Segment
Amazon CloudFront
Amazon CloudFront
Memcached
Memcached
Amazon ElastiCache
Amazon ElastiCache
Amazon RDS for Aurora
Amazon RDS for Aurora
MySQL
MySQL
Amazon RDS
Amazon RDS
Amazon S3
Amazon S3
Docker
Docker
Capistrano
Capistrano
AWS Elastic Beanstalk
AWS Elastic Beanstalk
Rails API
Rails API
Rails
Rails
Algolia
Algolia

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
Klipfolio logo

Klipfolio

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4
0
13
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+ 1
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An online dashboard platform for building real-time business dashboards
    Be the first to leave a pro
    Klipfolio logo
    Klipfolio
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    Data Studio logo
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    Tassanai Singprom
    Tassanai Singprom
    Web Developer | 9 upvotes 262.3K views
    Slack
    Slack
    BrowserStack
    BrowserStack
    Sentry
    Sentry
    Kibana
    Kibana
    Visual Studio Code
    Visual Studio Code
    npm
    npm
    GitLab
    GitLab
    GitHub
    GitHub
    Git
    Git
    Elasticsearch
    Elasticsearch
    Postman
    Postman
    Google Analytics
    Google Analytics
    MariaDB
    MariaDB
    GraphQL
    GraphQL
    Amazon RDS
    Amazon RDS
    Lumen
    Lumen
    Laravel
    Laravel
    Firebase
    Firebase
    Vue.js
    Vue.js
    Sass
    Sass
    Ubuntu
    Ubuntu
    Amazon EC2
    Amazon EC2
    Redis
    Redis
    jQuery
    jQuery
    HTML5
    HTML5
    PHP
    PHP
    JavaScript
    JavaScript

    This is my stack in Application & Data

    JavaScript PHP HTML5 jQuery Redis Amazon EC2 Ubuntu Sass Vue.js Firebase Laravel Lumen Amazon RDS GraphQL MariaDB

    My Utilities Tools

    Google Analytics Postman Elasticsearch

    My Devops Tools

    Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack

    My Business Tools

    Slack

    See more
    Jack Graves
    Jack Graves
    Head of Product Development at Automation Consultants | 5 upvotes 14.3K views
    atAutomation ConsultantsAutomation Consultants
    Google Analytics
    Google Analytics
    Segment
    Segment

    We currently use Google Analytics in our stack to monitor page views across our Marketplace listings, Documentation site and primary website. We're currently also investigating using Segment to provide in-app analytics in our Cloud App offerings. We expect to use Segment to guide our App development efforts, by monitoring, anonymously which features our clients use the most - but this is in early stages and will involve an update to our privacy policy.

    See more
    AzureDataStudio logo

    AzureDataStudio

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    5
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    6
    5
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    A cross-platform database tool for data professionals
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      AzureDataStudio logo
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      Data Studio
      Tableau logo

      Tableau

      217
      108
      0
      217
      108
      + 1
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      Tableau helps people see and understand data.
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        Tableau logo
        Tableau
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        Data Studio logo
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        Looker logo

        Looker

        160
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        6
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        6
        Pioneering the next generation of BI, data discovery & data analytics
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        Looker
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        Redash logo

        Redash

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        1
        124
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        Easily query an existing database, share the dataset and visualize it in different ways
        Redash logo
        Redash
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        Julien DeFrance
        Julien DeFrance
        Principal Software Engineer at Tophatter | 16 upvotes 400.6K views
        atSmartZipSmartZip
        Amazon DynamoDB
        Amazon DynamoDB
        Ruby
        Ruby
        Node.js
        Node.js
        AWS Lambda
        AWS Lambda
        New Relic
        New Relic
        Amazon Elasticsearch Service
        Amazon Elasticsearch Service
        Elasticsearch
        Elasticsearch
        Superset
        Superset
        Amazon Quicksight
        Amazon Quicksight
        Amazon Redshift
        Amazon Redshift
        Zapier
        Zapier
        Segment
        Segment
        Amazon CloudFront
        Amazon CloudFront
        Memcached
        Memcached
        Amazon ElastiCache
        Amazon ElastiCache
        Amazon RDS for Aurora
        Amazon RDS for Aurora
        MySQL
        MySQL
        Amazon RDS
        Amazon RDS
        Amazon S3
        Amazon S3
        Docker
        Docker
        Capistrano
        Capistrano
        AWS Elastic Beanstalk
        AWS Elastic Beanstalk
        Rails API
        Rails API
        Rails
        Rails
        Algolia
        Algolia

        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
        Power BI logo

        Power BI

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        A business analytics service
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          Chartio logo

          Chartio

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          6
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          6
          A powerful Business Intelligence tool anyone can use
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          Chartio
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          Periscope logo

          Periscope

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          9
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          Periscope plugs directly into your database and lets you run, save and share analyses over billions of data...
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          Shiny logo

          Shiny

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          An R package that makes it easy to build interactive web apps
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            Microsoft SSRS logo

            Microsoft SSRS

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            A server-based report generating software system
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              Google Datastudio logo

              Google Datastudio

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              A reporting and data visualization tool
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              Qlik Sense logo

              Qlik Sense

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              A business intelligence and visual analytics platform
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                GoodData logo

                GoodData

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                We help businesses monetize big data.
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                  QlikView logo

                  QlikView

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                  10
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                  A Business Intelligence platform for turning data into knowledge
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                    Cube.js logo

                    Cube.js

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                    Open Source Analytics Framework
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