Startups Using Postgres in Boston

Java – We use java for our core backend and middleware needs. We use it for data mining large volumes of player tracking data and for an in memory business intelligence engine.

MySQL and PostgreSQL – We use both relational DB’s in various products. They’re used in fairly similar and traditional ways. We’ve let teams pick the DB they’re more comfortable with for each app. We’ve used PostgreSQL mostly for storing user data and MySQL mostly for persisting our analytics warehouse.

NodeJS – We use Express/NodeJS for our front end web servers. This allows us to take advantage of sharing clientside and serverside code.

Polymer – We use Polymer and Web Components to help modularize our UI. This is relatively new for us, with the goal of increasing maintainability, reusability, and reduced development time.

React Native – We use react currently for iOS development. We chose React so that when we also create an android app that will be quick and still feel native on each mobile plaform.

Flask – we migrated to Flask from Django to increase the flexibility with which we build and manage our portfolio of workflow applications. We manage our own library of plugins and cookiecutters to enable efficient setup for new team members and team members picking up new projects.

React – we moved to React from Ractive as our JS framework for its performance, component oriented architecture, and server side rendering. React raises the level of abstraction in our front-end code base, making it more predictable and allowing our engineers to focus on building functionality versus wrestling with the framework.

AWS – as a healthcare IT company, we need to maintain a very high bar for our security and privacy infrastructure, given the high-stakes nature and stringent requirements of our large hospital system customer base. Our users rely on our ProviderMatch platform to match millions of patients to the right providers every year. AWS is our core cloud platform that allows us to accomplish this at scale.

ElasticSearch – our core patient-provider matching engine is enable by multi-faceted search. We have customized ES to our domain-specific data models, query types, and end user stories. ES recently migrated away from search as a core area of innovation (in lieu of analytics), so while we are investing in ways to optimize our utilization of ES, we also continue to keep an eye on the landscape of alternatives!

Slack – Slack is core to how our team communicates and gains critical insight into how our platform is performing. The ProviderMatch platform’s services are tightly integrated into Slack, and provide notifications and real-time performance insights that allow our team to stay on top of all platform activity.