Built in Boston Startup Tech Stacks

We asked startups with Boston-based engineering teams to share 5 highlights from their tech stack, and we also gathered information about technologies used from the job posts of over 200 of the startups we've curated on BSG. We're updating this regularly as new built-in-boston startups are added, new developer roles are being hired for, and startups send in updates.

Tech stack tags cover application development, DevOps, testing, and data science technologies these startups use: programming languages, related tools, frameworks, and popular libraries. We've done our best to keep tech tags standardized and updated, but if you're a developer interested in a particular startup you should always reach out to them for the most up-to-date information.

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Recruiting sourcing as a service, plus video interviews.

Tech Stack Highlights

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.

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KAYAK for car insurance. Quick, personalized car insurance quotes from multiple providers.

Tech Stack Highlights

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.

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Predictive analytics for healthcare data.

Tech Stack Highlights

Scala and Spark – Used for our batch processing backend and for machine learning pipelines. This stack provides us with the flexibility in data science approaches as we tackle new use cases and customers as well as the ease of integration in production.

Angular2, NodeJS, and Play! Framework – Our user-facing stack for both our frontend and REST API. As a startup, learning from users and reacting to their needs is the highest priority. Angular2 and node allows rapid development of new features with modern designs. Play, a scala web framework, provides our stable REST API for customers that want automated integration.

Amazon Web Services – Managing the sensitivity of healthcare data and would be a daunting task without existing vetted systems. We are able to leverage many services throughout to enable security and compliance — Cloudformation for reproducibility and configuration management, ACM for TLS, custom encryption with KMS, CloudWatch for logging and montoring, very granular IAM permissions, and so on.

ECS, Docker containers, and CircleCI – Continuous deployment at the onset was important to both the engineering team and the business operations. As Amazon’s ECS has matured the ease of managing docker containers across different environments (dev to prod) and growing with our scaling needs has been worth it. CircleCI’s allows us to integrate seamlessly with building git branches and running all of our unit tests, integration tests, and system tests.

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Cybersecurity platform to detect threats coming from compromised user accounts.

Tech Stack Highlights

Apache Spark – We use Spark, Spark Streaming, and the Apache Kafka frameworks for fast in-memory compute, real-time streaming, and lambda architecture. These technologies power our cyber threat detection, remediation and visualization software.

Cassandra – Our platform relies on Apache Cassandra NoSQL database for long-term data analytics and reporting. We use Elasticsearch for real-time search and analysis and Redis for in-memory cache.

Docker – We’re built on a Docker container micro-services architecture and Ansible DevOps orchestration framework for flexible bare-metal, virtual machine & cloud deployments.

Angular.js – We use the Angular front-end framework with D3.js, and NodeJS on the backend.

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Knowledgebase within your Slack to organize and share information with your teams.

Tech Stack Highlights

Draft.js & React – We recently rebuilt our text editor from the ground up on top of Draft.js. Building on Draft.js lets us create a smooth, rich editing experience that gets out of the user’s way so they can focus on sharing great content with their team. We also use Babel and Webpack to transpile and bundle up our front-end assets.

Slack – Tettra is built on top of the Slack platform. We use Slack for login and authentication and have built in notifications and slash commands. Our CEO even wrote an article about it.

PHP/Laravel – Our web application is built on the PHP web framework Laravel. Laravel comes with a ton of great building blocks including an ORM, queuing system, templating framework, and a prebuilt Vagrant box (VM) for local development to get us up and running and keep iterating quickly.

GitHub/Travis/Heroku – We use a combination of GitHub, Travis and Heroku for our continuous integration/deployment process. All pull requests get code-reviewed by a team member and have tests run automatically. Once code is merged to master, Travis runs the build and deploys to our staging environment on Heroku. We use the Heroku pipeline feature to promote staging code to production.

Intercom – At Tettra, everyone talks to customers. We use Intercom to get user feedback directly in the app, resolve bugs and inform our product process every day.

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Smart water cooler for less plastic waste, plus flavored drink dispensing options.

Tech Stack Highlights

At Bevi the software team is structured to have shared responsibility over all the code. Each team member works on many parts of our stack including Web Ui (React), Mobile Applications (Android), Firmware (Arduino), and Backend (Java 8). We got weekly sprints and do code reviews using git on bitbucket.

Android – The Bevi smart water coolers have an android tablet that is the main interaction point with our end users. We update and push our apps regularly and we create seasonal animations that our customers love. The android tablet also functions as an IoT device that relays all events to our backend. We often have to dig deep in the android OS to ensure the uptime of our machines.

InfluxDB – A time series database that we use to store the history of all our machines and all service data. We store our data as events in an append-only way.

Java8 with dropwizard and guava – Used to handle streams of data coming from the machines and create derived streams to compute the status of consumables and flag any abnormal behavior. The data is continuously used to optimize the working of the Bevi and the user experience. We go pretty far in using a functional programming style in java.

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Real-time admissions and discharge notifications link providers anywhere patients receive care.

Tech Stack Highlights

Spring Boot – We field a number of microservices on top of Spring Boot. Its convention-over-configuration design allows us to focus on business logic rather than plumbing. We’re particularly looking forward to the Spring team’s upcoming first-class support for Kotlin, which we’ve been gradually introducing as a safe, expressive alternative to Java 8.

React + Redux – We’ve built a highly interactive and engaging front-end using React and Redux. The resulting code is modular, easy to reason about, flexible, and composable.

Kafka – We use Kafka as our primary message bus. Unlike most “big data” technologies, Kafka has allowed us to scale without imposing a notable increase in complexity. In fact, becuase its append-only architecture allows us to view topic contents long after the message has been “consumed”, Kafka allows us to significantly improve monitoring and visibility over more traditional message buses (JMS, AMQP). We’re looking forward to experimenting with Kafka Streams as a lightweight alternative to standalone stream processing frameworks such as Spark.

Zeppelin – We use Apache Zeppelin to query, aggregate, and visualize data across a number of heterogeneous data sources, including MySQL, ElasticSearch, and S3. We write ‘notebooks’ in Scala and SQL to drive Spark in creating these visualizations. These notebooks can be ad hoc or shared, versioned, and parameterized.

NiFi – We use NiFi as an orchestration layer to manage real-time data flows in a simple scaleable way. The framework provides us with the ability to easily monitor the progress of messages as they move through the processing pipeline and to replay messages should it be necessary.

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Tech Stack Highlights

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.

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Commercial insurance search engine.

Tech Stack Highlights

Keen.io – Keen.io plays an essential role for us in user and application analytics. Our user facing and internal application event tracking dashboards are built on top of keen events. We utilize both client-side and Node.js libraries for event tracking and management.

Firebase/AngularFire – Firebase and AngularFire are used in the front end to create a dynamic, real-time user-interface, and to support user authentication.

Gitlab – We use a self-hosted gitlab instance running on Ubuntu for source code control and release management.

Node.js/Express – Node.js and Express are used for a variety of background services including job running and our REST API.

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Using AI / NLP to analyze your site’s content and find related topics for better search optimization & marketing.

Tech Stack Highlights

Amazon Web Services – We rely on AWS extensively. We use EC2 for computing, EBS for storage, RDS for relational databases, ElastiCache for caching, and we’ve also experimented with Kinesis, Lambda and ElastiSearch. In the past, we used other hosting providers that were less expensive, but as we’re scaling, AWS has been able to scale extremely effectively.

Codeship.io – We use Codeship to run integration tests at every commit. This lets us push hotfixes quickly, especially for UI changes, and lets us run experiments quickly.

FullStory – We use FullStory extensively to look at customers’ use of MarketMuse. It’s also great for debugging – when a customer reports an issue, we can replicate it in FullStory. Our Customer Success team also uses it, especially with new customers, to see how well they’re using the software and where there may be gaps in their understanding.

Scala with Play Framework – We use python for prototyping and in areas where we need to move flexibly. But for heavy lifting, we use Scala. It runs on the JVM, so it natively supports all Java libraries (of which there are many in Natural Language Processing). We use Akka and its “actors” extensively to take full advantage of CPU and network/IO resources.

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