Startups Using SQS in Boston

Ruby / Rails – We use Ruby and Rails for our web facing applications, as well as the API underlying the platform. The entire platform is deployed to Amazon AWS. We haven’t made the jump to Rails 5 yet for existing applications, but it’s on the radar.

AWS – The entire drizly platform is built on AWS. We use a variety of different services, including SNS, Redshift and Lambda, which are used heavily in our data pipeline.

React – Where possible new frontend development is done in React. We have some SPA style projects built in React, as well as a significant number of components on our e-commerce site utilizing react.

Mysql/Postgres/Redshift – Depending on the application we use a slightly different data-store. We like to be flexible, and find the right solution for the problem at hand.

SQS – We utilize SQS heavily as a background job processing system. We’ve found it to be stable, and durable, and performant enough for our use cases.

React/Redux – We use React for all new development, in combination with Redux for state management, and are actively converting existing features to it. The simplicity of React is more in parity with our Go back-end, and removes many of the frustrations our team has encountered with other technologies. We leverage Reselect for performance and composability, and find Enzyme to be a great testing utility.

AWS – The clypd platform runs entirely on AWS. We leverage VPCs to provide network isolation, EC2 to run our code and provide load balancing, S3 for backups, RDS to host databases, SQS for job queuing, and Redshift for heavy duty analytics. We have been thoughtful in how we set up security groups and instance profiles to follow a least-privilege model for both admins performing maintenance and our apps. We define the state of our infrastructure in Terraform, which allows for code review and dry-runs of infrastructure changes, along with maintaining history in git.

Go – Our team loves using Go to build software for the back-end. The safety and speed are a huge asset for rapid iteration. We’ve successfully leveraged many packages from the community, including Testify for unit test assertions and gorp for a minimal database abstraction layer.

GLPK – At its core, our product optimizes the buying and selling of television advertising. For that, we need technology that can grow and scale to accommodate larger and more complicated optimization problems. R has been crucial for the rapid prototyping of these mathematical problem abstractions but to actually solve them we leverage the Gnu Linear Programming Kit (GLPK). Both tools help us grow and refine the mathematical foundations of our product.