Alternatives to Google Datastudio logo

Alternatives to Google Datastudio

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

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.
Google Datastudio is a tool in the Business Intelligence category of a tech stack.

Top Alternatives to Google Datastudio

  • 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. ...

  • 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. ...

  • 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. ...

  • 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. ...

  • Dataform
    Dataform

    Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure. ...

  • 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. ...

  • 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. ...

Google Datastudio alternatives & related posts

Tableau logo

Tableau

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Tableau helps people see and understand data.
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PROS OF TABLEAU
  • 4
    Capable of visualising billions of rows
  • 1
    Intuitive and easy to learn
  • 1
    Responsive
CONS OF TABLEAU
  • 1
    Very expensive for small companies

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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.

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Metabase logo

Metabase

785
1.1K
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An open-source business intelligence tool
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PROS OF METABASE
  • 57
    Database visualisation
  • 42
    Open Source
  • 40
    Easy setup
  • 35
    Dashboard out of the box
  • 20
    Free
  • 14
    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
  • 5
    Harder to setup than similar tools

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Metabase logo

Metabase

785
1.1K
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An open-source business intelligence tool
785
1.1K
+ 1
256
PROS OF METABASE
  • 57
    Database visualisation
  • 42
    Open Source
  • 40
    Easy setup
  • 35
    Dashboard out of the box
  • 20
    Free
  • 14
    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
  • 5
    Harder to setup than similar tools

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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
  • 13
    Cross-filtering
  • 1
    Database visualisation
CONS OF POWER BI
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    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.

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    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
    • 2
      Price

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    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.

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    Dataform logo

    Dataform

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    A framework for managing SQL based data operations.
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    PROS OF DATAFORM
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        Data Studio logo

        Data Studio

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        Your data is powerful. Use it
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        PROS OF DATA STUDIO
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          CONS OF DATA STUDIO
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            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.

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            Superset logo

            Superset

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            Data exploration and visualization platform, by Airbnb
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            PROS OF SUPERSET
            • 10
              Awesome interactive filtering
            • 6
              Wide SQL database support
            • 6
              Free
            • 6
              Shareable & editable dashboards
            • 5
              Great for data collaborating on data exploration
            • 3
              User & Role Management
            • 3
              Easy to share charts & dasboards
            CONS OF SUPERSET
            • 3
              It is difficult to install on the server
            • 3
              Ugly GUI
            • 3
              Link diff db together "Data Modeling "

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            Julien DeFrance
            Principal Software Engineer at Tophatter · | 16 upvotes · 2.6M 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.

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