Load-driven Shard Distribution in Elasticsearch - Story of an Internship

Since July 2019 I have been an intern at Meltwater in Budapest, working in the Foundation team that is focused on developer productivity. It has been a truly valuable experience to solve challenging real-life problems, that have an impact on the everyday lives of our developers.

In this blogpost, I will share my experience as an intern at Meltwater, and discuss the details of the project that I have been working on.

Zalando & Meltwater kickoff Knowledge Sharing Around Managing Internal Software Delivery Platforms

In late August 2019 Meltwater had the pleasure of hosting Zalando in our Berlin office for a knowledge sharing session about applying product management for internal platforms that improve software delivery performance.

In this post we will explain how this meeting came to be, provide a sneak peak into the topics we covered, and what upcoming iterations of this exchange could look like.

Enriching 450M Docs Daily With a Boring Stream Processor

For our fairhair.ai platform we enrich over 450 million documents such as news articles and social posts per day, with a dependency tree of more than 20 NLP syntactic and semantic enrichment tasks. We ingest these documents as a continuous stream of data and guarantee delivery of enriched documents within 5 minutes of ingestion.

This technical feat required tight collaboration between two specialised teams: data science and platform engineering. Enabling both teams to efficiently work together around a common workflow execution engine was another problem we needed to solve. Hopefully that description fully piqued your interest because our solution (Benthos) is totally boring.

Deep Learning Models for Sentiment Analysis

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.