Being a Data Engineer is not only about moving the data but also about extracting value from it. Read an article on how we implemented anomalies detection for one of our clients that works in the podcasting industry, Acast. With the mechanisms we implemented, Acast employees are notified on the podcasts that suddenly gain or lose the audience. The results reveal trendsetting podcasts (the influencing ones, that grow episode after episode), hot topics (on shows that publish an episode that is listened all over the world) and also weird podcasts applications behaviour (sudden spikes in the listening pattern). All these anomalies notifications are based purely on the data and simple logarithm curve fitting - it's unbelievable what you can achieve with data&math!
Introduction We recently took part in the Kaggle H&M Personalized Fashion Recommendations competition where we were challenged to build a…
Read moreA tutorial on how to deploy one of the key pieces of the MLOps-enabling modern data platform: the Feature Store on Azure Databricks with Terraform as…
Read moreOne of our internal initiatives are GetInData Labs, the projects where we discover and work with different data tools. In the DataOps Labs, we’ve been…
Read morePlease dive in the third part of a blog series based on a project delivered for one of our clients. Please click part I, part II to read the…
Read moreDespite the era of GenAI hype, classical machine learning is still alive! Personally, I used to use ChatGPT (e.g. for idea generation), however I…
Read moreApache NiFi, a big data processing engine with graphical WebUI, was created to give non-programmers the ability to swiftly and codelessly create data…
Read moreTogether, we will select the best Big Data solutions for your organization and build a project that will have a real impact on your organization.
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