Meltwater has been providing sentiment analysis powered by machine-learning for more than 10 years. In 2009 we deployed our first models for English and German. Today, we support in-house models for 16 languages.

In this blog post we discuss how we use deep learning and feedback loops to deliver sentiment analysis at scale to more than 30 thousand customers.

You certainly like podcasts, don’t you? Jeff Campbell, an experienced Agile Coach working with Meltwater, recently joined forces with Vasco Duarte to record a series of podcasts for the Scrum Master Toolbox.

In the podcast series they chose a Q&A format, where the audience submits questions that Jeff and Vasco then discussed on the show.

One of our teams at Meltwater was recently faced with a problem that required relatively simple tasks applied to a large volume of data. To solve this we experimented with a pattern we call micro-pipelines, which are a sequence of microservices that work together to create efficient, fault tolerant systems.

This post provides an example of how we designed and built such a micro-pipeline.

Benthos Lab is a website where users of the Benthos stream processor can write, format, execute and share their pipeline configurations. This app is now a critical part of our Benthos workflow, but it would never have existed were it not for Meltwater Hackathons.

This post explains how we built Benthos Lab and how the Meltwater Hackathons have enabled us to do so.

Meltwater’s Foundation team has invested heavily in Kubernetes to provide this platform as a service to other development teams. Many of those teams have migrated to Kubernetes already.

Naturally our Foundation team learned a lot about what can, and will, go wrong when running a big enough Kubernetes cluster. Recently they attended Cloud Native Rejekts in Barcelona, where they shared some of these learnings.