Startups Using Java in Boston
Via their job posts and information submitted by startups themselves, these are the Boston Java startups we've found.
Interested in other technologies? Browse or search all of the built-in-boston tech stacks we've curated.
LinkedIn for blue collar workers / recruiting solutions for companies.
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.
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.
Home energy monitor that connects to your electric monitor and provides appliance by appliance data to a smartphone app.
Rail transportation search & ticketing technologies.