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Meteor vs Python: What are the differences?

What is Meteor? An ultra-simple, database-everywhere, data-on-the-wire, pure-Javascript web framework. A Meteor application is a mix of JavaScript that runs inside a client web browser, JavaScript that runs on the Meteor server inside a Node.js container, and all the supporting HTML fragments, CSS rules, and static assets.

What is Python? A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java. 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.

Meteor and Python are primarily classified as "Frameworks (Full Stack)" and "Languages" tools respectively.

"Real-time", "Full stack, one language" and "Best app dev platform available today" are the key factors why developers consider Meteor; whereas "Great libraries", "Readable code" and "Beautiful code" are the primary reasons why Python is favored.

Meteor and Python are both open source tools. It seems that Meteor with 41.1K GitHub stars and 5.03K forks on GitHub has more adoption than Python with 25K GitHub stars and 10.3K GitHub forks.

According to the StackShare community, Python has a broader approval, being mentioned in 2789 company stacks & 3501 developers stacks; compared to Meteor, which is listed in 195 company stacks and 152 developer stacks.

What is Meteor?

A Meteor application is a mix of JavaScript that runs inside a client web browser, JavaScript that runs on the Meteor server inside a Node.js container, and all the supporting HTML fragments, CSS rules, and static assets.

What is 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.
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What are some alternatives to Meteor and Python?
React
Lots of people use React as the V in MVC. Since React makes no assumptions about the rest of your technology stack, it's easy to try it out on a small feature in an existing project.
Angular 2
Angular is a development platform for building mobile and desktop web applications.
Node.js
Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices.
ASP.NET
.NET is a developer platform made up of tools, programming languages, and libraries for building many different types of applications.
Rails
Rails is a web-application framework that includes everything needed to create database-backed web applications according to the Model-View-Controller (MVC) pattern.
See all alternatives
Decisions about Meteor and Python
StackShare Editors
StackShare Editors
Kubernetes
Kubernetes
Go
Go
Python
Python

Following its migration from vanilla instances with autoscaling groups to Kubernetes, Postmates began facing challenges while “migrating workloads that needed to scale up very quickly.”

The built-in Horizontal Pod Autoscaler (HPA) automatically scales the number of pods in a replication controller, deployment or replica set based on observed CPU utilization. But the challenges for Postmates is that there’s no way to configure the scale velocity of one particular cluster with an HPA.

For Postmates, which runs at least three different types of applications with distinct performance and scaling characteristics, this proved problematic.

To overcome these challenges, the team created and open sourced the Configurable Horizontal Pod Autoscaler, which allows for fine-grained tuning on a per-HPA object basis. The result is that “you can configure critical services to scale down very slowly, while every other service could be configured to scale down instantly to reduce costs.”

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Hampton Catlin
Hampton Catlin
VP of Engineering at Rent The Runway · | 6 upvotes · 8.3K views
atRent the RunwayRent the Runway
Java
Java
Python
Python
Ruby
Ruby

At our company, and I've noticed a lot of other ones... application developers and dev-ops people tend to use Ruby and our statisticians and data scientists love Python . Like most companies, our stack is kind of split that way. Ruby is used as glue in most of our production systems ( Java being the main backend language), and then all of our data scientists and their various pipelines tend towards Python

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Ajit Parthan
Ajit Parthan
CTO at Shaw Academy · | 3 upvotes · 5.2K views
atShaw AcademyShaw Academy
Python
Python
PHP
PHP
#Etl

Multiple systems means there is a requirement to cart data across them.

Started off with Talend scripts. This was great as what we initially had were PHP/Python script - allowed for a more systematic approach to ETL.

But ended up with a massive repository of scripts, complex crontab entries and regular failures due to memory issues.

Using Stitch or similar services is a better approach: - no need to worry about the infrastructure needed for the ETL processes - a more formal mapping of data from source to destination as opposed to script developer doing his/her voodoo magic - lot of common sources and destination integrations are already builtin and out of the box

etl @{etlasaservice}|topic:1323|

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SVN (Subversion)
SVN (Subversion)
Git
Git
JSON
JSON
XML
XML
Python
Python
PHP
PHP
Java
Java
Swift
Swift
JavaScript
JavaScript
Linux
Linux
GitHub
GitHub
Visual Studio Code
Visual Studio Code

I use Visual Studio Code because at this time is a mature software and I can do practically everything using it.

  • It's free and open source: The project is hosted on GitHub and it’s free to download, fork, modify and contribute to the project.

  • Multi-platform: You can download binaries for different platforms, included Windows (x64), MacOS and Linux (.rpm and .deb packages)

  • LightWeight: It runs smoothly in different devices. It has an average memory and CPU usage. Starts almost immediately and it’s very stable.

  • Extended language support: Supports by default the majority of the most used languages and syntax like JavaScript, HTML, C#, Swift, Java, PHP, Python and others. Also, VS Code supports different file types associated to projects like .ini, .properties, XML and JSON files.

  • Integrated tools: Includes an integrated terminal, debugger, problem list and console output inspector. The project navigator sidebar is simple and powerful: you can manage your files and folders with ease. The command palette helps you find commands by text. The search widget has a powerful auto-complete feature to search and find your files.

  • Extensible and configurable: There are many extensions available for every language supported, including syntax highlighters, IntelliSense and code completion, and debuggers. There are also extension to manage application configuration and architecture like Docker and Jenkins.

  • Integrated with Git: You can visually manage your project repositories, pull, commit and push your changes, and easy conflict resolution.( there is support for SVN (Subversion) users by plugin)

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Ajit Parthan
Ajit Parthan
CTO at Shaw Academy · | 1 upvotes · 4K views
atShaw AcademyShaw Academy
Python
Python
PHP
PHP

Multiple systems means there is a requirement to cart data across them.

Started off with Talend scripts. This was great as what we initially had were PHP/Python script - allowed for a more systematic approach to ETL.

But ended up with a massive repository of scripts, complex crontab entries and regular failures due to memory issues.

Using Stitch or similar services is a better approach: - no need to worry about the infrastructure needed for the ETL processes - a more formal mapping of data from source to destination as opposed to script developer doing his/her voodoo magic - lot of common sources and destination integrations are already builtin and out of the box

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Eric Colson
Eric Colson
Chief Algorithms Officer at Stitch Fix · | 19 upvotes · 292.2K views
atStitch FixStitch Fix
Amazon EC2 Container Service
Amazon EC2 Container Service
Docker
Docker
PyTorch
PyTorch
R
R
Python
Python
Presto
Presto
Apache Spark
Apache Spark
Amazon S3
Amazon S3
PostgreSQL
PostgreSQL
Kafka
Kafka
#AWS
#Etl
#ML
#DataScience
#DataStack
#Data

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

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Python
Python
Django
Django
JavaScript
JavaScript
Node.js
Node.js

Django or NodeJS? Hi, I’m thinking about which software I should use for my web-app. What about Node.js or Django for the back-end? I want to create an online preparation course for the final school exams in my country. At the beginning for maths. The course should contain tutorials and a lot of exercises of different types. E.g. multiple choice, user text/number input and drawing tasks. The exercises should change (different levels) with the learning progress. Wrong questions should asked again with different numbers. I also want a score system and statistics. So far, I have got only limited web development skills. (some HTML, CSS, Bootstrap and Wordpress). I don’t know JavaScript or Python.

Possible pros for Python / Django: - easy syntax, easier to learn for me as a beginner - fast development, earlier release - libraries for mathematical and scientific computation

Possible pros for JavaScript / Node.js: - great performance, better choice for real time applications: user should get the answer for a question quickly

Which software would you use in my case? Are my arguments for Python/NodeJS right? Which kind of database would you use?

Thank you for your answer!

Node.js JavaScript Django Python

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Git
Git
Docker
Docker
NATS
NATS
JavaScript
JavaScript
TypeScript
TypeScript
PostgreSQL
PostgreSQL
Python
Python
Go
Go

Go is a high performance language with simple syntax / semantics. Although it is not as expressive as some other languages, it's still a great language for backend development.

Python is expressive and battery-included, and pre-installed in most linux distros, making it a great language for scripting.

PostgreSQL: Rock-solid RDBMS with NoSQL support.

TypeScript saves you from all nonsense semantics of JavaScript , LOL.

NATS: fast message queue and easy to deploy / maintain.

Docker makes deployment painless.

Git essential tool for collaboration and source management.

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Omar Melendrez
Omar Melendrez
Front-end developer · | 3 upvotes · 4.2K views
Python
Python
C#
C#
Node.js
Node.js
React
React
Vue.js
Vue.js
#Fullstack
#Vscode

I'm #Fullstack here and work with Vue.js, React and Node.js in some projects but also C# for other clients. Also started learning Python. And all this with just one tool!: #Vscode I have used Atom and Sublime Text in the past and they are very good too, but for me now is just vscode. I think the combination of vscode with the free available extensions that the community is creating makes a powerful tool and that's why vscode became the most popular IDE for software development. You can match it to your own needs in a couple of minutes. Did I mention you can style it your way? Amazing tool!

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Tom Klein
Tom Klein
CEO at Gentlent · | 4 upvotes · 30.5K views
atGentlentGentlent
Python
Python
Electron
Electron
Socket.IO
Socket.IO
Google Compute Engine
Google Compute Engine
TypeScript
TypeScript
ES6
ES6
Ubuntu
Ubuntu
PostgreSQL
PostgreSQL
React
React
nginx
nginx
Sass
Sass
HTML5
HTML5
PHP
PHP
Node.js
Node.js
JavaScript
JavaScript

Our most used programming languages are JavaScript / Node.js for it's lightweight and fast use, PHP because everyone knows it, HTML5 because you can't live without it and Sass to write great CSS. Occasionally, we use nginx as a web server and proxy, React for our UX, PostgreSQL as fast relational database, Ubuntu as server OS, ES6 and TypeScript for Node, Google Compute Engine for our infrastructure, and Socket.IO and Electron for specific use cases. We also use Python for some of our backends.

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Node.js
Node.js
Meteor
Meteor

Mixmax was originally built using Meteor as a single monolithic app. As more users began to onboard, we started noticing scaling issues, and so we broke out our first microservice: our Compose service, for writing emails and Sequences, was born as a Node.js service. Soon after that, we broke out all recipient searching and storage functionality to another Node.js microservice, our Contacts service. This practice of breaking out microservices in order to help our system more appropriately scale, by being more explicit about each microservice’s responsibilities, continued as we broke out numerous more microservices.

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AWS Elastic Beanstalk
AWS Elastic Beanstalk
AWS Elastic Load Balancing (ELB)
AWS Elastic Load Balancing (ELB)
nginx
nginx
Go
Go
Amazon EC2
Amazon EC2
Node.js
Node.js
Meteor
Meteor
Mixmax
Mixmax

As Mixmax began to scale super quickly, with more and more customers joining the platform, we started to see that the Meteor app was still having a lot of trouble scaling due to how it tried to provide its reactivity layer. To be honest, this led to a brutal summer of playing Galaxy container whack-a-mole as containers would saturate their CPU and become unresponsive. I’ll never forget hacking away at building a new microservice to relieve the load on the system so that we’d stop getting paged every 30-40 minutes. Luckily, we’ve never had to do that again! After stabilizing the system, we had to build out two more microservices to provide the necessary reactivity and authentication layers as we rebuilt our Meteor app from the ground up in Node.js. This also had the added benefit of being able to deploy the entire application in the same AWS VPCs. Thankfully, AWS had also released their ALB product so that we didn’t have to build and maintain our own websocket layer in Amazon EC2. All of our microservices, except for one special Go one, are now in Node with an nginx frontend on each instance, all behind AWS Elastic Load Balancing (ELB) or ALBs running in AWS Elastic Beanstalk.

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Praveen Mooli
Praveen Mooli
Technical Leader at Taylor and Francis · | 11 upvotes · 176.4K views
MongoDB Atlas
MongoDB Atlas
Amazon S3
Amazon S3
Amazon DynamoDB
Amazon DynamoDB
Amazon RDS
Amazon RDS
Serverless
Serverless
Docker
Docker
Terraform
Terraform
Travis CI
Travis CI
GitHub
GitHub
RxJS
RxJS
Angular 2
Angular 2
AWS Lambda
AWS Lambda
Amazon SQS
Amazon SQS
Amazon SNS
Amazon SNS
Amazon Kinesis Firehose
Amazon Kinesis Firehose
Amazon Kinesis
Amazon Kinesis
Flask
Flask
Python
Python
ExpressJS
ExpressJS
Node.js
Node.js
Spring Boot
Spring Boot
Java
Java
#Backend
#Microservices
#Eventsourcingframework
#Webapps
#Devops
#Data

We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

To build #Webapps we decided to use Angular 2 with RxJS

#Devops - GitHub , Travis CI , Terraform , Docker , Serverless

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PubNub
PubNub
asyncio
asyncio
JavaScript
JavaScript
Python
Python

I love Python and JavaScript . You can do the same JavaScript async operations in Python by using asyncio. This is particularly useful when you need to do socket programming in Python. With streaming sockets, data can be sent or received at any time. In case your Python program is in the middle of executing some code, other threads can handle the new socket data. Libraries like asyncio implement multiple threads, so your Python program can work in an asynchronous fashion. PubNub makes bi-directional data streaming between devices even easier.

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Helio Junior
Helio Junior
CSS 3
CSS 3
JavaScript
JavaScript
Python
Python
#DataScience
#UXdesign
#NodeJS
#Electron

Python is a excellent tool for #DataScience , but up to now is very poor in #uxdesign . To do some design I'm using JavaScript and #nodejs , #electron stack. The possibility of use CSS 3 to draw interfaces is very awesome and fast. Unfortunatelly Python don't have (yet) a good way to make a #UXdesign .

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Interest over time
Reviews of Meteor and Python
Avatar of MichelFloyd
Founder at cloak.ly
Review ofMeteorMeteor

I discovered Meteor thanks to my daughter who used it for a project at MIT. I was amazed at how much she had built in such a short time. I had also been trying to figure out how to build a browser-based crypto app so I jumped into Meteor and had an MVP for cloak.ly in a few short months starting from nothing. Learning Meteor really alters what you perceive as easy and difficult in full-stack development. It has an amazing ability to simplify your thinking and your code. Community support in terms of packages is outstanding as well which saves tremendous time. The quality of the software is outstanding with very few regressions cropping up during their frequent releases.

Being at the bleeding edge of the js community does have its downsides however. While early Meteor (with Blaze/handlebars templates) was exceedingly simple, Meteor have had to introduce support for both angular and react. In combination with the move to ECMAscript this has resulted in a lot of work for developers to just keep up with the evolution of the platform. Someone who was an expert 6 months ago might quickly find themselves being a newb again. If you're someone who doesn't like change you may want to stick to jQuery.

Living in the bay area I have the luxury of being able to attend Meteor events frequently. Having met many members of the MDG team, I have tremendous confidence in the future of the platform. This is a very solid group with a rare combination of broad vision and excellent execution.

Review ofMeteorMeteor

Meteor is my favorite framework. It makes everything fun. Syncing data across devices is really easy and you don't have to mess around with sockets at all. You can insert data into the database on the client. There's tons of security options. There's over 3000 packages on the packaging system. Instant iOS and Android apps. Amazing, reactive routing. Free hosting. Easy deployment with Meteor Up. What's not to like?

Review ofMeteorMeteor

Meteor is so powerful and flexible. I love it. In the near future, it will be the top-used framework.

Review ofMeteorMeteor

We have gone "all in" on Meteor and I recommend you do to.

How developers use Meteor and Python
Avatar of Exchange rates API
Exchange rates API uses PythonPython

Beautiful is better than ugly.

Explicit is better than implicit.

Simple is better than complex.

Complex is better than complicated.

Flat is better than nested.

Sparse is better than dense.

Readability counts.

Special cases aren't special enough to break the rules.

Although practicality beats purity.

Errors should never pass silently.

Unless explicitly silenced.

In the face of ambiguity, refuse the temptation to guess.

There should be one-- and preferably only one --obvious way to do it.

Although that way may not be obvious at first unless you're Dutch.

Now is better than never.

Although never is often better than right now.

If the implementation is hard to explain, it's a bad idea.

If the implementation is easy to explain, it may be a good idea.

Namespaces are one honking great idea -- let's do more of those!

Avatar of Web Dreams
Web Dreams uses PythonPython

To me, this is by far the best programming language. Why? Because it’s the only language that really got me going after trying to get into programming with Java for a while. Python is powerful, easy to learn, and gets you to unsderstand other languages more once you understand it. Did I state I love the python language? Well, I do..

Avatar of ttandon
ttandon uses PythonPython

Backend server for analysis of image samples from iPhone microscope lens. Chose this because of familiarity. The number one thing that I've learned at hackathons is that work exclusively with what you're 100% comfortable with. I use Python extensively at my day job at Wit.ai, so it was the obvious choice for the bulk of my coding.

Avatar of papaver
papaver uses PythonPython

been a pythoner for around 7 years, maybe longer. quite adept at it, and love using the higher constructs like decorators. was my goto scripting language until i fell in love with clojure. python's also the goto for most vfx studios and great for the machine learning. numpy and pyqt for the win.

Avatar of cloak.ly
cloak.ly uses MeteorMeteor

Without Meteor cloak.ly could not have been built as quickly by such a small team. Meteor was instrumental to getting an MVP up quickly and dealing with the complexities of browser-based encryption.

Avatar of Blood Bot
Blood Bot uses PythonPython

Large swaths of resources built for python to achieve natural language processing. (We are in the process of deprecating the services written in python and porting them over to Javascript and node)

Avatar of ShareThis
ShareThis uses MeteorMeteor

Built on Node.js, Meteor's real time reactivity and its wide package ecosystem allows us to quickly prototype and build apps in a lean way

Avatar of Giftstarter
Giftstarter uses MeteorMeteor

We would like to make magic with Meteor for the future of GiftStarter.

Avatar of Hooked
Hooked uses MeteorMeteor

Hooked is built with Meteor as the primary application framework.

Avatar of IVS
IVS uses MeteorMeteor

Typical buzz tech. Nothing practical in here.

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How much does Python cost?
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