| Comments

Increase Diversity by Reducing Biases in your Hiring Process

Would you agree that your biases are affecting your recruitment process? We have been thinking about it and we were especially curious how we can improve our recruitment process by working with our biases and learning how to disarm those when hiring.

In this post we are sharing the tools and processes that we found useful. You can try them too!

| Comments

Using Machine Learning to Load Balance Elasticsearch Queries

Meltwater recently launched the Fairhair.ai data science platform. Part of this platform are several large Elasticsearch clusters, which serve insights over billions of social media posts and editorial articles. The nature of the searches that our customers need to run against this data quickly make the default load balancing behaviour of Elasticsearch insufficient.

In this post we explain how we built a custom search router using machine learning, that helps us to address the shortcomings of Elasticsearch’s default round-robin approach, and greatly improves search performance and fault tolerance.

| Comments

Meltwater is Sponsoring Brewing Agile 2018

Meltwater is excited to sponsor Brewing Agile in Gothenburg on October 12-13, 2018. This is the 4th year in a row that Meltwater is supporting Brewing Agile, so you can tell that we are honestly excited about this event.

This is the only conference about Agile in Gothenburg, and there are still tickets available, so don’t wait and sign up quickly.

| Comments

Why We Built a Dull Stream Processor

Benthos is a dull and resilient stream processor that solves mundane streaming tasks. Its development is driven by our desire to defer these tasks to a common tool wherever sensible, allowing us to focus on solving more interesting business problems within our stream pipelines. This post explains why we chose to build it.

| Comments

Excel Friendly CSV Exports with Elixir

Love it or hate it, CSV is a format many developers have to deal with at some stage. It’s lightweight and it’s understood by many platforms and humans. However, if you’re using non-ASCII characters (like an umlaut on the word über or accent in José) and you have users viewing your CSV files in Microsoft Excel, things get a bit more complicated.

Most Excel users open CSV files simply by double-clicking the file and waiting for Excel to import it automatically. But if Excel doesn’t decode the CSV file correctly, your data will end up as a garbled mess.

How do we avoid this when generating CSV files in Elixir?