Systems | Development | Analytics | API | Testing

Some data problems are universal — like dealing with unstructured data. At Bystronic, a global leader in sheet metal processing, we have mountains of it. From technical documentation to sales decks, HR policies, and IT knowledge bases, data is scattered across folders, servers, and systems. Industry research shows that 80% of enterprise data is unstructured, meaning it’s often invisible to the teams that need it most. As a result, up to 68% of that data goes unused. The impact is real.

Data is the backbone of modern business, and in the dynamic retail landscape, harnessing this power through retail data analytics isn't just an advantage, it's essential. With the right business intelligence (BI) tool, retailers can transform vast amounts of transactional data into actionable insights, helping optimize operations significantly. Gone are the days when intuition alone sufficed.

For many organizations, achieving effective dashboard design is a recurring problem. Simply put, a lot of effort is likely going into creating dashboards your intended audience don’t even look at. However, it's a problem that can be easily remedied following a few core principles.

In certain sectors, such as healthcare data is not just valuable—it's SACRED. The same goes for data analytics in the healthcare sector, where the stakes are incredibly high. Healthcare data analytics consists of the systematic analysis of any health-related data that can be used not only to improve patient care, but also to find a way to improve healthcare ops and make informed decisions based on the analysis of said data.

In my last blog post, I looked at the essential first part of building any data pipeline—exploring the raw source data to understand its characteristics and relationships. The data is information about river levels, rainfall, and other weather information provided by the UK Environment Agency on a REST API. I used the HTTP Source connector to stream this into Apache Kafka topics (one per REST endpoint), and then Tableflow to expose these as Apache Iceberg tables.

By Erez Schnaider, Technical Product Marketing Manager, ClearML AI engineering today goes far beyond simply training a model. Teams are fine-tuning large language models on high-end GPUs, running massive, distributed experiments, and orchestrating hybrid workflows spanning on-premises clusters, private and public clouds. With great power comes great responsibility, and with powerful hardware comes complexity. Without robust controls, things can quickly descend into costly chaos: Who’s using what?