Alternatives to Power BI logo

Alternatives to Power BI

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

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

Top Alternatives to Power BI

  • DOMO
    DOMO

    Domo: business intelligence, data visualization, dashboards and reporting all together. Simplify your big data and improve your business with Domo's agile and mobile-ready platform. ...

  • 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. ...

  • 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. ...

  • Python
    Python

    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...

Power BI alternatives & related posts

DOMO logo

DOMO

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75
0
Domo optimizes your business by connecting you to the data, people, and expertise you need to improve business...
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+ 1
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PROS OF DOMO
    Be the first to leave a pro
    CONS OF DOMO
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      related DOMO posts

      QlikView logo

      QlikView

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      60
      0
      A Business Intelligence platform for turning data into knowledge
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      60
      + 1
      0
      PROS OF QLIKVIEW
        Be the first to leave a pro
        CONS OF QLIKVIEW
          Be the first to leave a con

          related QlikView posts

          Tableau logo

          Tableau

          1.3K
          1.3K
          8
          Tableau helps people see and understand data.
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          PROS OF TABLEAU
          • 6
            Capable of visualising billions of rows
          • 1
            Intuitive and easy to learn
          • 1
            Responsive
          CONS OF TABLEAU
          • 3
            Very expensive for small companies

          related Tableau 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
          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
          Looker logo

          Looker

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          639
          9
          Pioneering the next generation of BI, data discovery & data analytics
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          639
          + 1
          9
          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
          • 3
            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

          358.4K
          272.5K
          8.1K
          Lightweight, interpreted, object-oriented language with first-class functions
          358.4K
          272.5K
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          8.1K
          PROS OF JAVASCRIPT
          • 1.7K
            Can be used on frontend/backend
          • 1.5K
            It's everywhere
          • 1.2K
            Lots of great frameworks
          • 898
            Fast
          • 745
            Light weight
          • 425
            Flexible
          • 392
            You can't get a device today that doesn't run js
          • 286
            Non-blocking i/o
          • 237
            Ubiquitousness
          • 191
            Expressive
          • 55
            Extended functionality to web pages
          • 49
            Relatively easy language
          • 46
            Executed on the client side
          • 30
            Relatively fast to the end user
          • 25
            Pure Javascript
          • 21
            Functional programming
          • 15
            Async
          • 13
            Full-stack
          • 12
            Setup is easy
          • 12
            Future Language of The Web
          • 12
            Its everywhere
          • 11
            Because I love functions
          • 11
            JavaScript is the New PHP
          • 10
            Like it or not, JS is part of the web standard
          • 9
            Expansive community
          • 9
            Everyone use it
          • 9
            Can be used in backend, frontend and DB
          • 9
            Easy
          • 8
            Most Popular Language in the World
          • 8
            Powerful
          • 8
            Can be used both as frontend and backend as well
          • 8
            For the good parts
          • 8
            No need to use PHP
          • 8
            Easy to hire developers
          • 7
            Agile, packages simple to use
          • 7
            Love-hate relationship
          • 7
            Photoshop has 3 JS runtimes built in
          • 7
            Evolution of C
          • 7
            It's fun
          • 7
            Hard not to use
          • 7
            Versitile
          • 7
            Its fun and fast
          • 7
            Nice
          • 7
            Popularized Class-Less Architecture & Lambdas
          • 7
            Supports lambdas and closures
          • 6
            It let's me use Babel & Typescript
          • 6
            Can be used on frontend/backend/Mobile/create PRO Ui
          • 6
            1.6K Can be used on frontend/backend
          • 6
            Client side JS uses the visitors CPU to save Server Res
          • 6
            Easy to make something
          • 5
            Clojurescript
          • 5
            Promise relationship
          • 5
            Stockholm Syndrome
          • 5
            Function expressions are useful for callbacks
          • 5
            Scope manipulation
          • 5
            Everywhere
          • 5
            Client processing
          • 5
            What to add
          • 4
            Because it is so simple and lightweight
          • 4
            Only Programming language on browser
          • 1
            Test
          • 1
            Hard to learn
          • 1
            Test2
          • 1
            Not the best
          • 1
            Easy to understand
          • 1
            Subskill #4
          • 1
            Easy to learn
          • 0
            Hard 彤
          CONS OF JAVASCRIPT
          • 22
            A constant moving target, too much churn
          • 20
            Horribly inconsistent
          • 15
            Javascript is the New PHP
          • 9
            No ability to monitor memory utilitization
          • 8
            Shows Zero output in case of ANY error
          • 7
            Thinks strange results are better than errors
          • 6
            Can be ugly
          • 3
            No GitHub
          • 2
            Slow
          • 0
            HORRIBLE DOCUMENTS, faulty code, repo has bugs

          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 · 12.4M 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

          See more
          Git logo

          Git

          296.2K
          177.6K
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          Fast, scalable, distributed revision control system
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          PROS OF GIT
          • 1.4K
            Distributed version control system
          • 1.1K
            Efficient branching and merging
          • 959
            Fast
          • 845
            Open source
          • 726
            Better than svn
          • 368
            Great command-line application
          • 306
            Simple
          • 291
            Free
          • 232
            Easy to use
          • 222
            Does not require server
          • 27
            Distributed
          • 22
            Small & Fast
          • 18
            Feature based workflow
          • 15
            Staging Area
          • 13
            Most wide-spread VSC
          • 11
            Role-based codelines
          • 11
            Disposable Experimentation
          • 7
            Frictionless Context Switching
          • 6
            Data Assurance
          • 5
            Efficient
          • 4
            Just awesome
          • 3
            Github integration
          • 3
            Easy branching and merging
          • 2
            Compatible
          • 2
            Flexible
          • 2
            Possible to lose history and commits
          • 1
            Rebase supported natively; reflog; access to plumbing
          • 1
            Light
          • 1
            Team Integration
          • 1
            Fast, scalable, distributed revision control system
          • 1
            Easy
          • 1
            Flexible, easy, Safe, and fast
          • 1
            CLI is great, but the GUI tools are awesome
          • 1
            It's what you do
          • 0
            Phinx
          CONS OF GIT
          • 16
            Hard to learn
          • 11
            Inconsistent command line interface
          • 9
            Easy to lose uncommitted work
          • 7
            Worst documentation ever possibly made
          • 5
            Awful merge handling
          • 3
            Unexistent preventive security flows
          • 3
            Rebase hell
          • 2
            When --force is disabled, cannot rebase
          • 2
            Ironically even die-hard supporters screw up badly
          • 1
            Doesn't scale for big data

          related Git posts

          Simon Reymann
          Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 10.7M 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 · 9.5M 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.

          See more
          GitHub logo

          GitHub

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          Powerful collaboration, review, and code management for open source and private development projects
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          PROS OF GITHUB
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            Open source friendly
          • 1.5K
            Easy source control
          • 1.3K
            Nice UI
          • 1.1K
            Great for team collaboration
          • 867
            Easy setup
          • 504
            Issue tracker
          • 486
            Great community
          • 483
            Remote team collaboration
          • 451
            Great way to share
          • 442
            Pull request and features planning
          • 147
            Just works
          • 132
            Integrated in many tools
          • 121
            Free Public Repos
          • 116
            Github Gists
          • 112
            Github pages
          • 83
            Easy to find repos
          • 62
            Open source
          • 60
            It's free
          • 60
            Easy to find projects
          • 56
            Network effect
          • 49
            Extensive API
          • 43
            Organizations
          • 42
            Branching
          • 34
            Developer Profiles
          • 32
            Git Powered Wikis
          • 30
            Great for collaboration
          • 24
            It's fun
          • 23
            Clean interface and good integrations
          • 22
            Community SDK involvement
          • 20
            Learn from others source code
          • 16
            Because: Git
          • 14
            It integrates directly with Azure
          • 10
            Standard in Open Source collab
          • 10
            Newsfeed
          • 8
            It integrates directly with Hipchat
          • 8
            Fast
          • 8
            Beautiful user experience
          • 7
            Easy to discover new code libraries
          • 6
            Smooth integration
          • 6
            Cloud SCM
          • 6
            Nice API
          • 6
            Graphs
          • 6
            Integrations
          • 6
            It's awesome
          • 5
            Quick Onboarding
          • 5
            Reliable
          • 5
            Remarkable uptime
          • 5
            CI Integration
          • 5
            Hands down best online Git service available
          • 4
            Uses GIT
          • 4
            Version Control
          • 4
            Simple but powerful
          • 4
            Unlimited Public Repos at no cost
          • 4
            Free HTML hosting
          • 4
            Security options
          • 4
            Loved by developers
          • 4
            Easy to use and collaborate with others
          • 3
            Ci
          • 3
            IAM
          • 3
            Nice to use
          • 3
            Easy deployment via SSH
          • 2
            Easy to use
          • 2
            Leads the copycats
          • 2
            All in one development service
          • 2
            Free private repos
          • 2
            Free HTML hostings
          • 2
            Easy and efficient maintainance of the projects
          • 2
            Beautiful
          • 2
            Easy source control and everything is backed up
          • 2
            IAM integration
          • 2
            Very Easy to Use
          • 2
            Good tools support
          • 2
            Issues tracker
          • 2
            Never dethroned
          • 2
            Self Hosted
          • 1
            Dasf
          • 1
            Profound
          CONS OF GITHUB
          • 54
            Owned by micrcosoft
          • 38
            Expensive for lone developers that want private repos
          • 15
            Relatively slow product/feature release cadence
          • 10
            API scoping could be better
          • 9
            Only 3 collaborators for private repos
          • 4
            Limited featureset for issue management
          • 3
            Does not have a graph for showing history like git lens
          • 2
            GitHub Packages does not support SNAPSHOT versions
          • 1
            No multilingual interface
          • 1
            Takes a long time to commit
          • 1
            Expensive

          related GitHub posts

          Johnny Bell

          I was building a personal project that I needed to store items in a real time database. I am more comfortable with my Frontend skills than my backend so I didn't want to spend time building out anything in Ruby or Go.

          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.

          See more

          Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

          Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

          Check Out My Architecture: CLICK ME

          Check out the GitHub repo attached

          See more
          Python logo

          Python

          243.7K
          198.9K
          6.9K
          A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
          243.7K
          198.9K
          + 1
          6.9K
          PROS OF PYTHON
          • 1.2K
            Great libraries
          • 962
            Readable code
          • 847
            Beautiful code
          • 788
            Rapid development
          • 690
            Large community
          • 438
            Open source
          • 393
            Elegant
          • 282
            Great community
          • 272
            Object oriented
          • 220
            Dynamic typing
          • 77
            Great standard library
          • 60
            Very fast
          • 55
            Functional programming
          • 49
            Easy to learn
          • 45
            Scientific computing
          • 35
            Great documentation
          • 29
            Productivity
          • 28
            Easy to read
          • 28
            Matlab alternative
          • 24
            Simple is better than complex
          • 20
            It's the way I think
          • 19
            Imperative
          • 18
            Free
          • 18
            Very programmer and non-programmer friendly
          • 17
            Powerfull language
          • 17
            Machine learning support
          • 16
            Fast and simple
          • 14
            Scripting
          • 12
            Explicit is better than implicit
          • 11
            Ease of development
          • 10
            Clear and easy and powerfull
          • 9
            Unlimited power
          • 8
            It's lean and fun to code
          • 8
            Import antigravity
          • 7
            Print "life is short, use python"
          • 7
            Python has great libraries for data processing
          • 6
            Although practicality beats purity
          • 6
            Now is better than never
          • 6
            Great for tooling
          • 6
            Readability counts
          • 6
            Rapid Prototyping
          • 6
            I love snakes
          • 6
            Flat is better than nested
          • 6
            Fast coding and good for competitions
          • 6
            There should be one-- and preferably only one --obvious
          • 6
            High Documented language
          • 5
            Great for analytics
          • 5
            Lists, tuples, dictionaries
          • 4
            Easy to learn and use
          • 4
            Simple and easy to learn
          • 4
            Easy to setup and run smooth
          • 4
            Web scraping
          • 4
            CG industry needs
          • 4
            Socially engaged community
          • 4
            Complex is better than complicated
          • 4
            Multiple Inheritence
          • 4
            Beautiful is better than ugly
          • 4
            Plotting
          • 3
            Many types of collections
          • 3
            Flexible and easy
          • 3
            It is Very easy , simple and will you be love programmi
          • 3
            If the implementation is hard to explain, it's a bad id
          • 3
            Special cases aren't special enough to break the rules
          • 3
            Pip install everything
          • 3
            List comprehensions
          • 3
            No cruft
          • 3
            Generators
          • 3
            Import this
          • 3
            If the implementation is easy to explain, it may be a g
          • 2
            Can understand easily who are new to programming
          • 2
            Batteries included
          • 2
            Securit
          • 2
            Good for hacking
          • 2
            Better outcome
          • 2
            Only one way to do it
          • 2
            Because of Netflix
          • 2
            A-to-Z
          • 2
            Should START with this but not STICK with This
          • 2
            Powerful language for AI
          • 1
            Automation friendly
          • 1
            Sexy af
          • 1
            Slow
          • 1
            Procedural programming
          • 0
            Ni
          • 0
            Powerful
          • 0
            Keep it simple
          CONS OF PYTHON
          • 53
            Still divided between python 2 and python 3
          • 28
            Performance impact
          • 26
            Poor syntax for anonymous functions
          • 22
            GIL
          • 19
            Package management is a mess
          • 14
            Too imperative-oriented
          • 12
            Hard to understand
          • 12
            Dynamic typing
          • 12
            Very slow
          • 8
            Indentations matter a lot
          • 8
            Not everything is expression
          • 7
            Incredibly slow
          • 7
            Explicit self parameter in methods
          • 6
            Requires C functions for dynamic modules
          • 6
            Poor DSL capabilities
          • 6
            No anonymous functions
          • 5
            Fake object-oriented programming
          • 5
            Threading
          • 5
            The "lisp style" whitespaces
          • 5
            Official documentation is unclear.
          • 5
            Hard to obfuscate
          • 5
            Circular import
          • 4
            Lack of Syntax Sugar leads to "the pyramid of doom"
          • 4
            The benevolent-dictator-for-life quit
          • 4
            Not suitable for autocomplete
          • 2
            Meta classes
          • 1
            Training wheels (forced indentation)

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