Alternatives to Heap logo

Alternatives to Heap

Mixpanel, Google Analytics, Amplitude, JavaScript, and Git are the most popular alternatives and competitors to Heap.
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What is Heap and what are its top alternatives?

Heap is a powerful analytics tool that allows businesses to capture, analyze, and visualize customer data without the need for manual tracking or coding. With Heap, companies can track user interactions on websites and mobile apps, create custom events, and gain insights into user behavior to make data-driven decisions. However, one limitation of Heap is its pricing, which can be expensive for small businesses with limited budgets.

  1. Mixpanel: Mixpanel is a popular analytics tool that offers advanced segmentation, funnel analysis, and retention tracking features. Pros include a user-friendly interface and comprehensive event tracking capabilities. Cons include pricing based on data volume, which can be expensive for high-frequency businesses.
  2. Google Analytics: Google Analytics is a free analytics tool that provides a wide range of features such as user behavior analysis, conversion tracking, and e-commerce reporting. Pros include its integration with other Google marketing tools and its robust reporting capabilities. Cons may include complex setup for beginners and limited custom event tracking.
  3. Amplitude: Amplitude is a product analytics tool that helps businesses understand user behavior, track engagement metrics, and optimize conversion rates. Pros include real-time data analysis and user segmentation capabilities. Cons may include limited customization options for complex data tracking.
  4. Kissmetrics: Kissmetrics is a customer engagement platform that offers features like cohort analysis, customer journey tracking, and A/B testing. Pros include detailed customer insights and personalized reporting. Cons may include a learning curve for new users and limited integration options with other tools.
  5. Adobe Analytics: Adobe Analytics is an enterprise-level analytics solution that provides advanced data visualization, predictive analytics, and audience segmentation features. Pros include robust data governance and workflow capabilities. Cons may include high costs for smaller businesses and complex implementation requirements.
  6. Snowplow: Snowplow is an open-source event data platform that allows businesses to capture, validate, and enrich customer event data for analysis. Pros include flexibility for custom event tracking and data ownership. Cons may include the need for technical expertise to set up and maintain the platform.
  7. Crazy Egg: Crazy Egg is a heat mapping and user behavior analytics tool that helps companies understand how visitors interact with their websites. Pros include visual data representation and easy setup process. Cons may include limited integration options with other analytics tools.
  8. Pendo: Pendo is a product analytics platform that offers features like in-app surveys, user feedback collection, and user onboarding tracking. Pros include user-friendly interface and comprehensive user journey mapping. Cons may include limited data visualization options compared to other tools.
  9. Segment: Segment is a customer data platform that helps businesses collect, clean, and organize customer data from various sources for analysis. Pros include easy data integration with various tools and platforms. Cons may include pricing based on data volume, which can be expensive for high-growth businesses.
  10. Matomo: Matomo is an open-source web analytics platform that offers features like self-hosting options, GDPR compliance, and customizable data tracking. Pros include data ownership and control over data privacy. Cons may include limited support compared to paid analytics tools.

Top Alternatives to Heap

  • Mixpanel
    Mixpanel

    Mixpanel helps companies build better products through data. With our powerful, self-serve product analytics solution, teams can easily analyze how and why people engage, convert, and retain to improve their user experience. ...

  • Google Analytics
    Google Analytics

    Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications. ...

  • Amplitude
    Amplitude

    Amplitude provides scalable mobile analytics that helps companies leverage data to create explosive user growth. Anyone in the company can use Amplitude to pinpoint the most valuable behavioral patterns within hours. ...

  • 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. ...

  • jQuery
    jQuery

    jQuery is a cross-platform JavaScript library designed to simplify the client-side scripting of HTML. ...

Heap alternatives & related posts

Mixpanel logo

Mixpanel

7.1K
3.7K
438
Powerful, self-serve product analytics to help you convert, engage, and retain more users
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3.7K
+ 1
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PROS OF MIXPANEL
  • 144
    Great visualization ui
  • 108
    Easy integration
  • 78
    Great funnel funcionality
  • 58
    Free
  • 22
    A wide range of tools
  • 15
    Powerful Graph Search
  • 11
    Responsive Customer Support
  • 2
    Nice reporting
CONS OF MIXPANEL
  • 2
    Messaging (notification, email) features are weak
  • 2
    Paid plans can get expensive
  • 1
    Limited dashboard capabilities

related Mixpanel posts

Max Musing
Founder & CEO at BaseDash · | 8 upvotes · 351K views

Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).

Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.

See more
Yasmine de Aranda
Chief Growth Officer at Huddol · | 7 upvotes · 368.5K views

Hi there, we are a seed-stage startup in the personal development space. I am looking at building the marketing stack tool to have an accurate view of the user experience from acquisition through to adoption and retention for our upcoming React Native Mobile app. We qualify for the startup program of Segment and Mixpanel, which seems like a good option to get rolling and scale for free to learn how our current 60K free members will interact in the new subscription-based platform. I was considering AppsFlyer for attribution, and I am now looking at an affordable yet scalable Mobile Marketing tool vs. building in-house. Braze looks great, so does Leanplum, but the price points are 30K to start, which we can't do. I looked at OneSignal, but it doesn't have user flow visualization. I am now looking into Urban Airship and Iterable. Any advice would be much appreciated!

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Google Analytics logo

Google Analytics

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48.4K
5K
Enterprise-class web analytics.
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48.4K
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PROS OF GOOGLE ANALYTICS
  • 1.5K
    Free
  • 926
    Easy setup
  • 890
    Data visualization
  • 698
    Real-time stats
  • 405
    Comprehensive feature set
  • 181
    Goals tracking
  • 154
    Powerful funnel conversion reporting
  • 138
    Customizable reports
  • 83
    Custom events try
  • 53
    Elastic api
  • 14
    Updated regulary
  • 8
    Interactive Documentation
  • 3
    Google play
  • 2
    Industry Standard
  • 2
    Walkman music video playlist
  • 2
    Advanced ecommerce
  • 1
    Medium / Channel data split
  • 1
    Easy to integrate
  • 1
    Financial Management Challenges -2015h
  • 1
    Lifesaver
  • 1
    Irina
CONS OF GOOGLE ANALYTICS
  • 11
    Confusing UX/UI
  • 8
    Super complex
  • 6
    Very hard to build out funnels
  • 4
    Poor web performance metrics
  • 3
    Very easy to confuse the user of the analytics
  • 2
    Time spent on page isn't accurate out of the box

related Google Analytics posts

Alex Step

We used to use Google Analytics to get audience insights while running a startup and we are constantly doing experiments to lear our users. We are a small team and we have a lack of time to keep up with trends. Here is the list of problems we are experiencing: - Analytics takes too much time - We have enough time to regularly monitor analytics - Google Analytics interface is too advanced and complicated - It's difficult to detect anomalies and trends in GA

We considered other solutions on a market, but found 2 main issues: - The solution created for analytic experts - The solution is pretty expensive and non-automated

After learning this fact we decided to create AI-powered Slack bot to analyze Google Analytics and share trends. The bot is currently working and highlights trends for us.

We are thinking about publishing this solution as a SaaS. If you are interested in automating Google Analytics analysis, drop a comment and you'll get an early access.

We will implement this solution only if we have 20+ early adaptors. Leave a message with your thought. I appreciate any feedback.

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Tim Specht
‎Co-Founder and CTO at Dubsmash · | 14 upvotes · 949.4K views

In order to accurately measure & track user behaviour on our platform we moved over quickly from the initial solution using Google Analytics to a custom-built one due to resource & pricing concerns we had.

While this does sound complicated, it’s as easy as clients sending JSON blobs of events to Amazon Kinesis from where we use AWS Lambda & Amazon SQS to batch and process incoming events and then ingest them into Google BigQuery. Once events are stored in BigQuery (which usually only takes a second from the time the client sends the data until it’s available), we can use almost-standard-SQL to simply query for data while Google makes sure that, even with terabytes of data being scanned, query times stay in the range of seconds rather than hours. Before ingesting their data into the pipeline, our mobile clients are aggregating events internally and, once a certain threshold is reached or the app is going to the background, sending the events as a JSON blob into the stream.

In the past we had workers running that continuously read from the stream and would validate and post-process the data and then enqueue them for other workers to write them to BigQuery. We went ahead and implemented the Lambda-based approach in such a way that Lambda functions would automatically be triggered for incoming records, pre-aggregate events, and write them back to SQS, from which we then read them, and persist the events to BigQuery. While this approach had a couple of bumps on the road, like re-triggering functions asynchronously to keep up with the stream and proper batch sizes, we finally managed to get it running in a reliable way and are very happy with this solution today.

#ServerlessTaskProcessing #GeneralAnalytics #RealTimeDataProcessing #BigDataAsAService

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Amplitude logo

Amplitude

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User analytics to fuel explosive user growth
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PROS OF AMPLITUDE
  • 11
    Great for product managers
  • 8
    Easy setup
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    Efficient analysis
  • 2
    Behavioral cohorts
  • 2
    Event streams for individual users
  • 2
    Chart edits get their own URLs
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    Free for up to 10M user actions per month
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    Fast
  • 1
    Great UI
  • 1
    Engagement Matrix is super helpful
CONS OF AMPLITUDE
  • 4
    Super expensive once you're past the free plan

related Amplitude posts

Jesus Dario Rivera Rubio
Telecomm Engineering at Netbeast · | 15 upvotes · 432.2K views

This time I want to share something different. For those that have read my stack decisions, it's normal to expect some advice on infrastructure or React Native. Lately my mind has been focusing more on product as a experience than what's it made of (anatomy). As a tech leader, I have to worry about things like: are we taking enough time for reviews? Are we improving over time? Are we faster now? Is our code of higher quality?

For all these questions you can add many great recommendations on your pipeline. We use Trello for bug-tracking and project management. We use https://danger.systems/js/ to add checks for linting, type-enforcing and other quality dimensions in our PRs and a great feature from Vercel that let's you previsualize deployments directly in a PR. However it's not easy to measure this improvements over time. For customer matters we have Amplitude or Firebase analytics, but for our internal process? That's a little bit more complicated.

I collaborated recently with some folks in a small startup as an early adopter to create a metrics dashboard for engineers. I tried to add the tool to stackshare.io but still it doesn't appear as one of the options, please take a look on it over product hunt and let us know https://www.producthunt.com/posts/scope-6

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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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8.1K
Lightweight, interpreted, object-oriented language with first-class functions
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PROS OF JAVASCRIPT
  • 1.7K
    Can be used on frontend/backend
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    It's everywhere
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    Lots of great frameworks
  • 896
    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
    Its everywhere
  • 12
    Future Language of The Web
  • 12
    Setup is easy
  • 11
    JavaScript is the New PHP
  • 11
    Because I love functions
  • 10
    Like it or not, JS is part of the web standard
  • 9
    Expansive community
  • 9
    Can be used in backend, frontend and DB
  • 9
    Easy
  • 9
    Everyone use it
  • 8
    Most Popular Language in the World
  • 8
    Can be used both as frontend and backend as well
  • 8
    Powerful
  • 8
    For the good parts
  • 8
    No need to use PHP
  • 8
    Easy to hire developers
  • 7
    Love-hate relationship
  • 7
    Agile, packages simple to use
  • 7
    Its fun and fast
  • 7
    Hard not to use
  • 7
    Nice
  • 7
    Versitile
  • 7
    Evolution of C
  • 7
    Photoshop has 3 JS runtimes built in
  • 7
    It's fun
  • 7
    Popularized Class-Less Architecture & Lambdas
  • 7
    Supports lambdas and closures
  • 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
    It let's me use Babel & Typescript
  • 6
    Easy to make something
  • 5
    What to add
  • 5
    Clojurescript
  • 5
    Stockholm Syndrome
  • 5
    Function expressions are useful for callbacks
  • 5
    Scope manipulation
  • 5
    Everywhere
  • 5
    Client processing
  • 5
    Promise relationship
  • 4
    Because it is so simple and lightweight
  • 4
    Only Programming language on browser
  • 1
    Easy to learn
  • 1
    Not the best
  • 1
    Hard to learn
  • 1
    Easy to understand
  • 1
    Test
  • 1
    Test2
  • 1
    Subskill #4
  • 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

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.

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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

289.9K
174.3K
6.6K
Fast, scalable, distributed revision control system
289.9K
174.3K
+ 1
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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 · 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.
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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

279.6K
243.9K
10.3K
Powerful collaboration, review, and code management for open source and private development projects
279.6K
243.9K
+ 1
10.3K
PROS OF GITHUB
  • 1.8K
    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
  • 482
    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
  • 53
    Owned by micrcosoft
  • 37
    Expensive for lone developers that want private repos
  • 15
    Relatively slow product/feature release cadence
  • 10
    API scoping could be better
  • 8
    Only 3 collaborators for private repos
  • 3
    Limited featureset for issue management
  • 2
    GitHub Packages does not support SNAPSHOT versions
  • 2
    Does not have a graph for showing history like git lens
  • 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.

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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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Python logo

Python

239.6K
195.6K
6.9K
A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
239.6K
195.6K
+ 1
6.9K
PROS OF PYTHON
  • 1.2K
    Great libraries
  • 961
    Readable code
  • 846
    Beautiful code
  • 787
    Rapid development
  • 689
    Large community
  • 435
    Open source
  • 393
    Elegant
  • 282
    Great community
  • 272
    Object oriented
  • 220
    Dynamic typing
  • 77
    Great standard library
  • 59
    Very fast
  • 55
    Functional programming
  • 49
    Easy to learn
  • 45
    Scientific computing
  • 35
    Great documentation
  • 29
    Productivity
  • 28
    Easy to read
  • 28
    Matlab alternative
  • 23
    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
    Flat is better than nested
  • 6
    Great for tooling
  • 6
    Rapid Prototyping
  • 6
    Readability counts
  • 6
    High Documented language
  • 6
    I love snakes
  • 6
    Fast coding and good for competitions
  • 6
    There should be one-- and preferably only one --obvious
  • 6
    Now is better than never
  • 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
    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
    It is Very easy , simple and will you be love programmi
  • 3
    Many types of collections
  • 3
    If the implementation is easy to explain, it may be a g
  • 2
    Batteries included
  • 2
    Should START with this but not STICK with This
  • 2
    Powerful language for AI
  • 2
    Can understand easily who are new to programming
  • 2
    Flexible and easy
  • 2
    Good for hacking
  • 2
    A-to-Z
  • 2
    Because of Netflix
  • 2
    Only one way to do it
  • 2
    Better outcome
  • 1
    Sexy af
  • 1
    Slow
  • 1
    Securit
  • 0
    Ni
  • 0
    Powerful
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)

related Python posts

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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Nick Parsons
Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 3.5M views

Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

#FrameworksFullStack #Languages

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jQuery logo

jQuery

190.2K
66.9K
6.6K
The Write Less, Do More, JavaScript Library.
190.2K
66.9K
+ 1
6.6K
PROS OF JQUERY
  • 1.3K
    Cross-browser
  • 957
    Dom manipulation
  • 809
    Power
  • 660
    Open source
  • 610
    Plugins
  • 459
    Easy
  • 395
    Popular
  • 350
    Feature-rich
  • 281
    Html5
  • 227
    Light weight
  • 93
    Simple
  • 84
    Great community
  • 79
    CSS3 Compliant
  • 69
    Mobile friendly
  • 67
    Fast
  • 43
    Intuitive
  • 42
    Swiss Army knife for webdev
  • 35
    Huge Community
  • 11
    Easy to learn
  • 4
    Clean code
  • 3
    Because of Ajax request :)
  • 2
    Powerful
  • 2
    Nice
  • 2
    Just awesome
  • 2
    Used everywhere
  • 1
    Improves productivity
  • 1
    Javascript
  • 1
    Easy Setup
  • 1
    Open Source, Simple, Easy Setup
  • 1
    It Just Works
  • 1
    Industry acceptance
  • 1
    Allows great manipulation of HTML and CSS
  • 1
    Widely Used
  • 1
    I love jQuery
CONS OF JQUERY
  • 6
    Large size
  • 5
    Sometimes inconsistent API
  • 5
    Encourages DOM as primary data source
  • 2
    Live events is overly complex feature

related jQuery posts

Kir Shatrov
Engineering Lead at Shopify · | 22 upvotes · 1.9M views

The client-side stack of Shopify Admin has been a long journey. It started with HTML templates, jQuery and Prototype. We moved to Batman.js, our in-house Single-Page-Application framework (SPA), in 2013. Then, we re-evaluated our approach and moved back to statically rendered HTML and vanilla JavaScript. As the front-end ecosystem matured, we felt that it was time to rethink our approach again. Last year, we started working on moving Shopify Admin to React and TypeScript.

Many things have changed since the days of jQuery and Batman. JavaScript execution is much faster. We can easily render our apps on the server to do less work on the client, and the resources and tooling for developers are substantially better with React than we ever had with Batman.

#FrameworksFullStack #Languages

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Ganesa Vijayakumar
Full Stack Coder | Technical Lead · | 19 upvotes · 4.7M views

I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.

I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).

As per my work experience and knowledge, I have chosen the followings stacks to this mission.

UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.

Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.

Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.

Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.

Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.

Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.

Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.

Happy Coding! Suggestions are welcome! :)

Thanks, Ganesa

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