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Python, Flask, React, React Native, AWS, Postgres, Redis, Docker, Terraform, PyTorch

We put a lot of care into how we ship code. Shipping fast starts with serious investment in tooling, like continuous deployment with multiple pushes per day. Shipping sustainably is about more than just effective work – it requires commitment to creating an environment where people can focus and get creative. Our tech stack includes:

React JS, TypeScript, Scala, Node.js, AWS, Graphite, Redis, DynamoDb, Kubernetes, MySQL, Python, Amazon Redshift, Swift, Kotlin, GraphQL, Swagger, ZooKeeper, Envoy

Core: Python Frontend: react, typescript, 3js Backend: PostgresQL, AWS 3D graphics: openGL, C++

Airtable’s core stack is written using JavaScript, and augmented with static type checking and other tools. We use Node.js on the backend, and mostly React on the front end, but our application requirements drive a substantial amount of custom software architecture; that is, Airtable is pretty far from a boilerplate “React + Express” application. Mobile apps are currently native and use JavaScript in webviews where appropriate. Our data infrastructure, which drives our data science and business intelligence efforts, is primarily in Python. We periodically evaluate adding other languages into our codebase for ecosystem or performance reasons; philosophically, we try hard to standardize on a few languages and toolchains so that investments in tooling can be leveraged across larger fractions of our engineering effort. Our production infrastructure runs on a variety of AWS services, principally using Terraform for deployment; for logging, aggregation, monitoring, and alerting, we use a mixture of ELK and some carefully selected third-party services.

Docker, GoLang, Rust, Python, Vue

NextJS, React, Typescript, GraphQL, Kafka, Python, Django

Rails 5.2, Ruby 2.3.7, Python, R, PostgreSQL 9.6, React 0.17, AWS, Docker, RabbitMQ, Sidekiq, Snowflake

Ribbon makes a broad use of modern technologies across its data platform, including but not limited to: AWS (S3, Lambda functions for scaling ML compute, Aurora/Postgres, EC2), Databricks for data processing, Postgres database, Django application for API with a managed elastic search service, GitHub for code management, PyCharm for code editing and management, Python (and PySpark) for programming, Datadog for monitoring

TypeScript, React, Apollo GraphQL, PostgresQL, Python

Python (using Flask / SQLAlchemy), PostgreSQL, React/Redux, Android (native, Kotlin), AWS

Infrastructure: AWS, Cloudwatch, Kubernetes, Ingress, Helm, Route53, Nginx, Jenkins, Sentry, RDS / PostgreSQL, Redis, Istio.

Backend: Python 3, Flask, Redis, RDS / PostgreSQL, Peewee, Nginx, Kubernetes, Docker.

Frontend: TypeScript, Vue.js, Capacitor, SASS, Vuetify.

Frontend: React Native (TypeScript), React (TypeScript). Backend: Django + Celery (Python), Redis, Heroku, AWS S3, AWS Aurora (PostgreSQL).

Programming: Python, Flask, pipenv, JSON Data: PostgreSQL, Vault, ActiveMQ Infrastructure: Docker, Terraform, Ansible, AWS, Linux, immutable

Frontend: Typescript, React… Backend: Python…

React.js, React Native, Typescript, Python, Zappa, Serverless Framework, Django, AWS, PostgreSQL, Kinesis, S3, Glue, Jest, Selenium, Pytest

Python, JavaScript, Ruby, Unix, Linux, React.js, Ruby-on-Rails, Ajax, Docker, SQL, Apache, Amazon-web-services, DataDog

Node.js / Typescript, Python, React, Postgres, GraphQL with Hasura, BigQuery (columnar), PyTorch, Pandas, Jupyter, Spark, PySpark, Keras + Tensorflow, XGBoost, LightGBM, Kubernetes, Docker, GCP