Alternatives to Knowi logo

Alternatives to Knowi

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

It combines Business Intelligence with integrated machine learning for advanced analytics, reporting and visualizations on structured and unstructured data.
Knowi is a tool in the Business Intelligence category of a tech stack.

Top Alternatives to Knowi

  • 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. ...

  • Kibana

    Kibana

    Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch. ...

  • 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. ...

  • Data Studio

    Data Studio

    Unlock the power of your data with interactive dashboards and engaging reports that inspire smarter business decisions. It鈥檚 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. ...

Knowi alternatives & related posts

Tableau logo

Tableau

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

    related Tableau posts

    Kibana logo

    Kibana

    13.5K
    10.2K
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    Explore & Visualize Your Data
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    PROS OF KIBANA
    • 87
      Easy to setup
    • 61
      Free
    • 44
      Can search text
    • 21
      Has pie chart
    • 13
      X-axis is not restricted to timestamp
    • 8
      Easy queries and is a good way to view logs
    • 6
      Supports Plugins
    • 3
      Dev Tools
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      More "user-friendly"
    • 3
      Can build dashboards
    • 2
      Easy to drill-down
    • 2
      Out-of-Box Dashboards/Analytics for Metrics/Heartbeat
    • 1
      Up and running
    CONS OF KIBANA
    • 5
      Unintuituve
    • 3
      Elasticsearch is huge
    • 3
      Works on top of elastic only
    • 2
      Hardweight UI

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    Tymoteusz Paul
    Devops guy at X20X Development LTD | 21 upvotes 路 4.3M views

    Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

    It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

    I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

    We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

    If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

    The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

    Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

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    Patrick Sun
    Software Engineer at Stitch Fix | 11 upvotes 路 431.7K views

    Elasticsearch's built-in visualization tool, Kibana, is robust and the appropriate tool in many cases. However, it is geared specifically towards log exploration and time-series data, and we felt that its steep learning curve would impede adoption rate among data scientists accustomed to writing SQL. The solution was to create something that would replicate some of Kibana's essential functionality while hiding Elasticsearch's complexity behind SQL-esque labels and terminology ("table" instead of "index", "group by" instead of "sub-aggregation") in the UI.

    Elasticsearch's API is really well-suited for aggregating time-series data, indexing arbitrary data without defining a schema, and creating dashboards. For the purpose of a data exploration backend, Elasticsearch fits the bill really well. Users can send an HTTP request with aggregations and sub-aggregations to an index with millions of documents and get a response within seconds, thus allowing them to rapidly iterate through their data.

    See more
    Metabase logo

    Metabase

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    An open-source business intelligence tool
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    PROS OF METABASE
    • 51
      Database visualisation
    • 41
      Open Source
    • 38
      Easy setup
    • 32
      Dashboard out of the box
    • 17
      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
    • 3
      Easy set up
    • 2
      Google analytics integration
    CONS OF METABASE
    • 3
      Harder to setup than similar tools

    related Metabase posts

    Metabase logo

    Metabase

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    An open-source business intelligence tool
    589
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    237
    PROS OF METABASE
    • 51
      Database visualisation
    • 41
      Open Source
    • 38
      Easy setup
    • 32
      Dashboard out of the box
    • 17
      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
    • 3
      Easy set up
    • 2
      Google analytics integration
    CONS OF METABASE
    • 3
      Harder to setup than similar tools

    related Metabase posts

    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
    • 5
      Cross-filtering
    CONS OF POWER BI
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      related Power BI posts

      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
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        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.

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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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            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
            • 3
              Open Source
            • 3
              SQL Friendly
            CONS OF REDASH
            • 1
              All results are loaded into RAM before displaying
            • 1
              Memory Leaks

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