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React, TypeScript, Apollo, GraphQL, Ruby on Rails, Redis, PostgreSQL, AWS

Golang, React, Redux, MongoDB, ElasticSearch, Redis, Kinesis, NATS, TimescaleDB, Lambda, Node.js, Postgres, Typescript

React, GraphQL, Go, Kubernetes, Docker, PostgreSQL, TypeScript

Node.js, TypeScript, Postgres, AWS, Terraform, Ansible, React, GraphQL

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

Qualified is a modern web app with a React UI interfacing over GraphQL, REST, and WebSockets with a Ruby on Rails backend. The data and messaging layers are built on PostgreSQL, Redis, and ClickHouse.

Backend/API: Ruby on Rails 6.1

Database: PostgreSQL

iOS: Native (Swift)

Android: Native (Kotlin)

Hosted on: AWS, deployed as containers on ECS

CI/CD: CircleCI pipeline‍

Frontend: Clojurescript, React, GraphQL

Backend: Clojure, JVM

Infrastructure: Kafka, Docker, Kubernetes, Packer, Terraform, AWS

Data: BigQuery, Tableau, Apache Beam

Go for backend services, Node and Typescript for our web services, Next.js and React/Typescript on the frontend. We run on AWS with Kubernetes for containerization, and gRPC and protobufs for internal service communication.

Airtable’s core stack is written using JavaScript, augmented with static type checking and other tools. We use Node.js on the backend, and mostly React on the frontend, 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.

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

Node.js & Express backend, Golang CLI, Postgres database, Google Cloud

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

Backend: Node + Express, Typescript, Go Microservices. Frontend: Next.js, React + Hooks, Storybook. Infrastructure: Heroku + AWS, CircleCI, DataDog.

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

Swift, JavaScript, TypeScript, Native iOS, UIKit, Combine, Node.js, Vapor Swift, React, Next.js, Postgres, MongoDB

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