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Startups Using Spring in Boston

Via their job posts and information submitted by startups themselves, these are the Boston Spring startups we've found.

Interested in other technologies? Browse or search all of the built-in-boston tech stacks we've curated.

Cybersecurity tools manager, providing higher-level coordination and performance analysis for orgs using multiple tools.

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Automating common data entry tasks for commercial lenders.

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“Bill of Materials management for manufacturing companies, hardware startups and distributed teams.”

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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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Matches app-wielding street teams with product companies who want better insight & promotion from their retail partners.

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Marketplace for event organizers to hire event help.

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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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“Mobile fundraising platform for silent auctions and raffles.” Allows for easy credit card payments & metrics for organizers.

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Rail transportation search & ticketing technologies.

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Remote doctor visits through video streaming.

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