Alternatives to Qlik Sense logo

Alternatives to Qlik Sense

QlikView, Tableau, Sisense, Power BI, and Looker are the most popular alternatives and competitors to Qlik Sense.
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What is Qlik Sense and what are its top alternatives?

Qlik Sense is a powerful business intelligence tool that enables users to create interactive visualizations and dashboards for data analysis. Its key features include intuitive drag-and-drop interface, in-memory data processing, and the ability to connect to multiple data sources. However, some limitations of Qlik Sense include its steep learning curve for beginners and the need for technical expertise to maximize its full potential.

  1. Tableau: Tableau is a popular data visualization tool known for its easy-to-use interface and robust feature set. Key features include drag-and-drop functionality, powerful analytics capabilities, and a large community for support. Pros of Tableau include its user-friendly interface and broad range of data connectors, while cons include its high pricing for enterprise features.
  2. Microsoft Power BI: Microsoft Power BI is a leading BI tool that offers data visualization, self-service analytics, and AI capabilities. Key features include seamless integration with Microsoft products, advanced analytics tools, and easy collaboration with Power BI service. Pros of Power BI include its familiarity for Microsoft users and cost-effective pricing, while cons include limitations in handling large datasets.
  3. Looker: Looker is a modern BI platform that offers data exploration and data visualization for businesses. Key features include data modeling, data discovery, and embedded analytics. Pros of Looker include its robust data modeling capabilities and data-driven insights, while cons include its limited customization options for visualizations.
  4. Domo: Domo is a cloud-based BI platform that offers real-time data visualization and analytics solutions. Key features include customizable dashboards, data integration, and collaboration tools. Pros of Domo include its intuitive interface and ability to handle large datasets, while cons include its dependency on internet connectivity for functionality.
  5. Sisense: Sisense is a business intelligence software that provides data analysis and visualization tools for organizations. Key features include in-chip technology, data preparation, and AI-powered analytics. Pros of Sisense include its fast data processing and scalable architecture, while cons include its pricing for advanced features.
  6. Zoho Analytics: Zoho Analytics is a self-service BI and analytics tool that helps businesses create interactive dashboards and reports. Key features include data blending, predictive analytics, and embedded analytics. Pros of Zoho Analytics include its affordability for small businesses and ease of use, while cons include limitations in handling complex data relationships.
  7. Yellowfin BI: Yellowfin BI is an analytics and business intelligence platform that offers data visualization, storytelling, and collaboration features. Key features include automated insights, mobile BI, and AI-powered analytics. Pros of Yellowfin BI include its strong visualization capabilities and user-friendly interface, while cons include its lower market recognition compared to other BI tools.
  8. GoodData: GoodData is a cloud-based BI platform that helps businesses create data-driven insights and analytics. Key features include embedded analytics, machine learning, and data governance tools. Pros of GoodData include its scalable architecture and customization options, while cons include its complexity in setting up and configuring data sources.
  9. Mode Analytics: Mode Analytics is a collaborative analytics platform that offers SQL-based querying, Python notebooks, and interactive visualizations. Key features include version control, data governance, and advanced analytics libraries. Pros of Mode Analytics include its ease of collaboration and advanced analytics capabilities, while cons include a learning curve for non-technical users.
  10. Pyramid Analytics: Pyramid Analytics is an enterprise BI platform that offers self-service analytics, governance, and security features. Key features include multi-authoring, AI-driven analytics, and dynamic report distribution. Pros of Pyramid Analytics include its robust security and governance capabilities, while cons include its complexity in setting up and administration.

Top Alternatives to Qlik Sense

  • QlikView
    QlikView

    It is a business discovery platform that provides self-service BI for all business users in organizations. With this tool, you can analyze data and use your data discoveries to support decision making. ...

  • 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. ...

  • Sisense
    Sisense

    It is making business intelligence (BI) analytics easy with its simple drag-and-drop and scalable end-to-end BI processes that help to prepare, analyze, and visualize multiple complex datasets quickly. ...

  • 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. ...

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

  • Git
    Git

    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. ...

  • GitHub
    GitHub

    GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over three million people use GitHub to build amazing things together. ...

Qlik Sense alternatives & related posts

QlikView logo

QlikView

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A Business Intelligence platform for turning data into knowledge
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PROS OF QLIKVIEW
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      related QlikView posts

      Tableau logo

      Tableau

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      Tableau helps people see and understand data.
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      CONS OF TABLEAU
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        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.

      See more
      Shared insights
      on
      TableauTableauQlikQlikPowerBIPowerBI

      Hello everyone,

      My team and I are currently in the process of selecting a Business Intelligence (BI) tool for our actively developing company, which has over 500 employees. We are considering open-source options.

      We are keen to connect with a Head of Analytics or BI Analytics professional who has extensive experience working with any of these systems and is willing to share their insights. Ideally, we would like to speak with someone from companies that have transitioned from proprietary BI tools (such as PowerBI, Qlik, or Tableau) to open-source BI tools, or vice versa.

      If you have any contacts or recommendations for individuals we could reach out to regarding this matter, we would greatly appreciate it. Additionally, if you are personally willing to share your experiences, please feel free to reach out to me directly. Thank you!

      See more
      Sisense logo

      Sisense

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      Simple drag-and-drop and scalable end-to-end BI processes
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      PROS OF SISENSE
        Be the first to leave a pro
        CONS OF SISENSE
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          related Sisense posts

          Power BI logo

          Power BI

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          Empower team members to discover insights hidden in your data
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            Cross-filtering
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            Powerful Calculation Engine
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            Access from anywhere
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            Intuitive and complete internal ETL
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            Database visualisation
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            Azure Based Service
          CONS OF POWER BI
            Be the first to leave a con

            related Power BI posts

            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.

            See more

            Which among the two, Kyvos and Azure Analysis Services, should be used to build a Semantic Layer?

            I have to build a Semantic Layer for the data warehouse platform and use Power BI for visualisation and the data lies in the Azure Managed Instance. I need to analyse the two platforms and find which suits best for the same.

            See more
            Looker logo

            Looker

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            Pioneering the next generation of BI, data discovery & data analytics
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            PROS OF LOOKER
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              Real time in app customer chat support
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              GitHub integration
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              Reduces the barrier of entry to utilizing data
            CONS OF LOOKER
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              Price

            related Looker posts

            Ankit Sobti

            Looker , Stitch , Amazon Redshift , dbt

            We recently moved our Data Analytics and Business Intelligence tooling to Looker . It's already helping us create a solid process for reusable SQL-based data modeling, with consistent definitions across the entire organizations. Looker allows us to collaboratively build these version-controlled models and push the limits of what we've traditionally been able to accomplish with analytics with a lean team.

            For Data Engineering, we're in the process of moving from maintaining our own ETL pipelines on AWS to a managed ELT system on Stitch. We're also evaluating the command line tool, dbt to manage data transformations. Our hope is that Stitch + dbt will streamline the ELT bit, allowing us to focus our energies on analyzing data, rather than managing it.

            See more
            Robert Zuber

            Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

            We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

            See more
            JavaScript logo

            JavaScript

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              Its everywhere
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              Future Language of The Web
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              Setup is easy
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              JavaScript is the New PHP
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              Because I love functions
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              Like it or not, JS is part of the web standard
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              Expansive community
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              Can be used in backend, frontend and DB
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              Easy
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              Everyone use it
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              Most Popular Language in the World
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              Can be used both as frontend and backend as well
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              Powerful
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              For the good parts
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              No need to use PHP
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              Easy to hire developers
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              Love-hate relationship
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              Its fun and fast
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              Can be used on frontend/backend/Mobile/create PRO Ui
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              1.6K Can be used on frontend/backend
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              It let's me use Babel & Typescript
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              Easy to make something
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              What to add
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              Horribly inconsistent
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              Shows Zero output in case of ANY error
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              Thinks strange results are better than errors
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              Can be ugly
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              Slow

            related JavaScript posts

            Zach Holman

            Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.

            But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.

            But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

            Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

            See more
            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.1M views

            How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

            Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

            Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

            https://eng.uber.com/distributed-tracing/

            (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

            Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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            Git logo

            Git

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              Fast
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              Open source
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              Better than svn
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              Great command-line application
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              Free
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              Compatible
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              Rebase supported natively; reflog; access to plumbing
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              Light
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              Fast, scalable, distributed revision control system
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              Easy
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              CLI is great, but the GUI tools are awesome
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            CONS OF GIT
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              Unexistent preventive security flows
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              Rebase hell
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              Ironically even die-hard supporters screw up badly
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              Doesn't scale for big data

            related Git posts

            Simon Reymann
            Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.3M views

            Our whole DevOps stack consists of the following tools:

            • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
            • Respectively Git as revision control system
            • SourceTree as Git GUI
            • Visual Studio Code as IDE
            • CircleCI for continuous integration (automatize development process)
            • Prettier / TSLint / ESLint as code linter
            • SonarQube as quality gate
            • Docker as container management (incl. Docker Compose for multi-container application management)
            • VirtualBox for operating system simulation tests
            • Kubernetes as cluster management for docker containers
            • Heroku for deploying in test environments
            • nginx as web server (preferably used as facade server in production environment)
            • SSLMate (using OpenSSL) for certificate management
            • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
            • PostgreSQL as preferred database system
            • Redis as preferred in-memory database/store (great for caching)

            The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

            • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
            • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
            • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
            • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
            • Scalability: All-in-one framework for distributed systems.
            • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
            See more
            Tymoteusz Paul
            Devops guy at X20X Development LTD · | 23 upvotes · 8.3M 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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            GitHub logo

            GitHub

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              Community SDK involvement
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              Because: Git
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              It integrates directly with Azure
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              Newsfeed
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              Fast
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              CI Integration
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              Uses GIT
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              Version Control
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              Security options
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              Ci
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              Leads the copycats
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              All in one development service
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              Free private repos
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              Free HTML hostings
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              Easy and efficient maintainance of the projects
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              Beautiful
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              Easy source control and everything is backed up
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              IAM integration
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              Very Easy to Use
            • 2
              Good tools support
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              Issues tracker
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              Never dethroned
            • 2
              Self Hosted
            • 1
              Dasf
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              Profound
            CONS OF GITHUB
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              Owned by micrcosoft
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              Expensive for lone developers that want private repos
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              Relatively slow product/feature release cadence
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              API scoping could be better
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              Only 3 collaborators for private repos
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              Limited featureset for issue management
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              GitHub Packages does not support SNAPSHOT versions
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              Does not have a graph for showing history like git lens
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              No multilingual interface
            • 1
              Takes a long time to commit
            • 1
              Expensive

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            Johnny Bell

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            I stumbled on Firebase by #Google, and it was really all I needed. It had realtime data, an area for storing file uploads and best of all for the amount of data I needed it was free!

            I built out my application using tools I was familiar with, React for the framework, Redux.js to manage my state across components, and styled-components for the styling.

            Now as this was a project I was just working on in my free time for fun I didn't really want to pay for hosting. I did some research and I found Netlify. I had actually seen them at #ReactRally the year before and deployed a Gatsby site to Netlify already.

            Netlify was very easy to setup and link to my GitHub account you select a repo and pretty much with very little configuration you have a live site that will deploy every time you push to master.

            With the selection of these tools I was able to build out my application, connect it to a realtime database, and deploy to a live environment all with $0 spent.

            If you're looking to build out a small app I suggest giving these tools a go as you can get your idea out into the real world for absolutely no cost.

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            Russel Werner
            Lead Engineer at StackShare · | 32 upvotes · 2.2M views

            StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.

            Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!

            #StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit

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