Startups Using Python in Boston

React.js – We use both React and Vue for our front end work. We’ve been quite happy with React but are even happier with Vue for it’s flexibility and because it’s model is more familiar.

PostgreSQL – We use Postgres as our database. Postgres is most attractive for us do to it’s strict SQL compliance as well as for Postgis, which we will be using for some of our product. We’re also using the Postgres JSON functionality for some portions of our application which are not easily modeled using a relational database.

Python – Our backend is written entirely in Python, which we find to be highly productive and elegant. We use Django as our backend framework. We get a lot of value from the ORM as well as from Django REST framework – and from the Django admin.

Aurelia – After tinkering with a number of different front-end Javascript frameworks, we’ve moved forward with Aurelia to enable rich standards-based client-side functionality, routing and data-binding. We find that Aurelia’s test-ability and modular design work well for our small team.

ASP.NET MVC/Web API – Using these enterprise-grade frameworks and Visual Studio enables us to focus on delivering value to our customers through new web-based products and features. Documentation and IDE support for .NET and C# is incredibly rich. We also depend heavily upon continuous integration (CI), one-click builds, and Selenium automation tests to build in quality from the start.

Elasticsearch – We’re using Elasticsearch as our search platform to enable educators to construct and perform complex queries to identify and visualize data about students, such as those that might need extra attention or are at risk. Student data, especially for English Language Learners (ELLs), is incredibly nuanced and is comprised of many different properties. Elasticsearch helps us scale to support the fast searching of multiple data sets from each of our school district customers.

Python – We find that it’s one of the best technologies for data wrangling, especially when using packages such as pandas. Whether it is manipulating large datasets in MongoDB via map-reduce functions, or transforming data files containing important student data, Python is our tool of choice in this area.

Octopus Deploy – We use Octopus Deploy for our deployment automation platform. It holds a near and dear place in our hearts because it provides first-class support for Windows/.NET and is extensible, open and reliable. And it’s from Australia!

Python – We’re using python for our core app, with Django/DRF powering our REST API, NLTK for NLP, and pandas running high-performance real time data analysis to calculate things like RateRank and savings estimates. We use Vagrant and Ansible for IT automation, and Jenkins and Selenium for QA automation and deployment to our AWS environment.

MariaDB – Our database runs Maria on RDS, for optimal MySQL-syntax performance. We crunch a lot of data in each query, so performance is key. Some of our queries approach 100 lines long, with multiple nested queries, dozens of joins, and layered aggregation, and we run some queries thousands of times per day.

Backbone – Backbone provided us “just enough” structure for our highly custom front-end MVC, while allowing us to build our own proprietary routing & workflow engine around it. We’re using epoxy for 2-way data-binding, and jQuery + Bootstrap plugins, in addition to dozens of proprietary UI components.

Bootstrap – Our mobile-first-responsive CSS uses Bootstrap as a baseline, but builds upon it to form a highly-customized, well-organized extensible style-guide with our own unique components and layouts. We’re using SASS class-extension, selector-nesting, and custom mixins under the hood to generate our CSS.