Startups Using Elasticsearch in Boston
Via their job posts and information submitted by startups themselves, these are the Boston Elasticsearch startups we've found.
Interested in other technologies? Browse or search all of the built-in-boston tech stacks we've curated.
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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.
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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