Alternatives to Interana logo

Alternatives to Interana

Tableau, Amplitude, Metabase, Looker, and Data Studio are the most popular alternatives and competitors to Interana.
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What is Interana and what are its top alternatives?

Interana is a solution for exploring, monitoring, and sharing data about your product, customers, and business. Run it on premises or in the cloud, share with tens to thousands of co-workers, and scale from millions to trillions of events.
Interana is a tool in the Business Intelligence category of a tech stack.

Interana alternatives & related posts

Tableau logo

Tableau

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108
0
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Tableau helps people see and understand data.
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    related Amplitude posts

    Yonas Beshawred
    Yonas Beshawred
    CEO at StackShare · | 4 upvotes · 41.5K views
    atStackShareStackShare
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    Google Analytics
    Segment
    Segment
    Amplitude
    Amplitude
    #FunnelAnalysisAnalytics
    #Analytics
    #Analyticsstack

    Adopting Amplitude was one of the best decisions we've made. We didn't try any of the alternatives- the free tier was really generous so it was easy to justify trying it out (via Segment). We've had Google Analytics since inception, but just for logged out traffic. We knew we'd need some sort of #FunnelAnalysisAnalytics solution, so it came down to just a few solutions.

    We had heard good things about Amplitude from friends and even had a consultant/advisor who was an Amplitude pro from using it as his company, so he kinda convinced us to splurge on the Enterprise tier for the behavioral cohorts alone. Writing the queries they provide via a few clicks in their UI would take days/weeks to craft in SQL. The behavioral cohorts allow us to create a lot of useful retention charts.

    Another really useful feature is kinda minor but kinda not. When you change a saved chart, a new URL gets generated and is visible in your browser (chartURL/edit) and that URL is immediately available to share with your team. It may sound inconsequential, but in practice, it makes it really easy to share and iterate on graphs. Only complaint is that you have to explicitly tag other team members as owners of whatever chart you're creating for them to be able to edit it and save it. I can see why this is the case, but more often than not, the people I'm sharing the chart with are the ones I want to edit it 🤷🏾‍♂️

    The Engagement Matrix feature is also really helpful (once you filter out the noisy events). Charts and dashboards are also great and make it easy for us to focus on the important metrics. We've been using Amplitude in production for about 6 months now. There's a bunch of other features we don't use regularly like Pathfinder, etc that I personally don't fully understand yet but I'm sure we'll start using them eventually.

    Again, haven't tried any of the alternatives like Heap, Mixpanel, or Kissmetrics so can't speak to those, but Amplitude works great for us.

    #analytics analyticsstack

    See more
    Looker logo

    Looker

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    6
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    Pioneering the next generation of BI, data discovery & data analytics
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    Data Studio logo

    Data Studio

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    Your data is powerful. Use it
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      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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      related Superset posts

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      Julien DeFrance
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      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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      Power BI logo

      Power BI

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        Chartio logo

        Chartio

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        A powerful Business Intelligence tool anyone can use
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        Periscope logo

        Periscope

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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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        Amazon Quicksight logo

        Amazon Quicksight

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        39
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        Fast, easy to use business analytics at 1/10th the cost of traditional BI solutions
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        Julien DeFrance
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        atSmartZipSmartZip
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        Node.js
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        AWS Lambda
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        New Relic
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        Amazon Elasticsearch Service
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        Elasticsearch
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        Superset
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        Amazon Quicksight
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        Amazon Redshift
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        Zapier
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        Segment
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        Amazon CloudFront
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        Memcached
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        Amazon ElastiCache
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        Amazon RDS for Aurora
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        MySQL
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        Amazon RDS
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        Amazon S3
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        Docker
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        Capistrano
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        AWS Elastic Beanstalk
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        Rails API
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        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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                  Cube.js

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                  Accelerating Data Analytics to New Heights
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