Alternatives to Looker logo

Alternatives to Looker

Tableau, Superset, Periscope, Chartio, and Mode are the most popular alternatives and competitors to Looker.
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What is Looker and what are its top alternatives?

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

Looker alternatives & related posts

Tableau logo

Tableau

227
116
0
227
116
+ 1
0
Tableau helps people see and understand data.
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    Tableau
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    Looker logo
    Looker

    related Superset posts

    Julien DeFrance
    Julien DeFrance
    Principal Software Engineer at Tophatter | 16 upvotes 491.5K views
    atSmartZipSmartZip
    Rails
    Rails
    Rails API
    Rails API
    AWS Elastic Beanstalk
    AWS Elastic Beanstalk
    Capistrano
    Capistrano
    Docker
    Docker
    Amazon S3
    Amazon S3
    Amazon RDS
    Amazon RDS
    MySQL
    MySQL
    Amazon RDS for Aurora
    Amazon RDS for Aurora
    Amazon ElastiCache
    Amazon ElastiCache
    Memcached
    Memcached
    Amazon CloudFront
    Amazon CloudFront
    Segment
    Segment
    Zapier
    Zapier
    Amazon Redshift
    Amazon Redshift
    Amazon Quicksight
    Amazon Quicksight
    Superset
    Superset
    Elasticsearch
    Elasticsearch
    Amazon Elasticsearch Service
    Amazon Elasticsearch Service
    New Relic
    New Relic
    AWS Lambda
    AWS Lambda
    Node.js
    Node.js
    Ruby
    Ruby
    Amazon DynamoDB
    Amazon DynamoDB
    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.

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    Periscope logo

    Periscope

    41
    43
    9
    41
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    + 1
    9
    Periscope plugs directly into your database and lets you run, save and share analyses over billions of data...
    Periscope logo
    Periscope
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    Chartio logo

    Chartio

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    6
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    30
    + 1
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    A powerful Business Intelligence tool anyone can use
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    Chartio
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    DOMO logo

    DOMO

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    11
    9
    + 1
    0
    Domo optimizes your business by connecting you to the data, people, and expertise you need to improve business...
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      Power BI logo

      Power BI

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      42
      0
      58
      42
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      0
      A business analytics service
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        Power BI logo
        Power BI
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        Looker logo
        Looker
        Data Studio logo

        Data Studio

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        112
        0
        149
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        + 1
        0
        Your data is powerful. Use it
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          Data Studio logo
          Data Studio
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          Redash logo

          Redash

          129
          99
          1
          129
          99
          + 1
          1
          Easily query an existing database, share the dataset and visualize it in different ways
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          Amazon Quicksight logo

          Amazon Quicksight

          39
          45
          1
          39
          45
          + 1
          1
          Fast, easy to use business analytics at 1/10th the cost of traditional BI solutions
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          related Amazon Quicksight posts

          Julien DeFrance
          Julien DeFrance
          Principal Software Engineer at Tophatter | 16 upvotes 491.5K views
          atSmartZipSmartZip
          Rails
          Rails
          Rails API
          Rails API
          AWS Elastic Beanstalk
          AWS Elastic Beanstalk
          Capistrano
          Capistrano
          Docker
          Docker
          Amazon S3
          Amazon S3
          Amazon RDS
          Amazon RDS
          MySQL
          MySQL
          Amazon RDS for Aurora
          Amazon RDS for Aurora
          Amazon ElastiCache
          Amazon ElastiCache
          Memcached
          Memcached
          Amazon CloudFront
          Amazon CloudFront
          Segment
          Segment
          Zapier
          Zapier
          Amazon Redshift
          Amazon Redshift
          Amazon Quicksight
          Amazon Quicksight
          Superset
          Superset
          Elasticsearch
          Elasticsearch
          Amazon Elasticsearch Service
          Amazon Elasticsearch Service
          New Relic
          New Relic
          AWS Lambda
          AWS Lambda
          Node.js
          Node.js
          Ruby
          Ruby
          Amazon DynamoDB
          Amazon DynamoDB
          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
          Shiny logo

          Shiny

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          0
          36
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          0
          An R package that makes it easy to build interactive web apps
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            Google Datastudio logo

            Google Datastudio

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            17
            2
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            Microsoft SSRS logo

            Microsoft SSRS

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            A server-based report generating software system
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              Qlik Sense logo

              Qlik Sense

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              5
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              19
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              A business intelligence and visual analytics platform
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                GoodData logo

                GoodData

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                18
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                17
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                0
                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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                    Power BI Embedded logo

                    Power BI Embedded

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                    Embedded BI reporting engine for your application
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                      Power BI Embedded logo
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                      PeriscopeData logo

                      PeriscopeData

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                      Accelerating Data Analytics to New Heights
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                        PeriscopeData logo
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                        Looker