Databricks

The LLM world has never been busier. The part I follow most closely, honestly, is the open weight one. On 1 September 2026, on the Artificial Analysis Intelligence Index, Kimi K3 and GLM-5.3 sit at 60 points against the 63 of Claude Opus 5 and the 61 of GPT-5.6 Sol. They have not taken the top spot, and Claude Fable 5.1 has just landed above everyone at 66 points, so the frontier has moved again in the meantime. Still, they are three points off Opus 5, next to nothing compared with the gap we were used to. A word of warning, I don’t want to dwell on benchmarks. I have tried these models myself, and the improvements are there. ...

On April 17, I took the stage at Global Azure Veneto with a dear friend. We share a passion for what we do and for the technology we use every day: he is a Chief Network Architect, and I am a Data Solution Architect. We have different skill sets, often working in separate departments that struggle to communicate with one another. It is precisely this disconnect that motivated us to create this session together. All too often, those who design data architectures are unfamiliar with the network infrastructure on which they run, and those who design the infrastructure lack full awareness of the requirements of an enterprise data platform. We see the result in projects: network decisions made without considering the serverless pattern, or data architectures designed without knowing how Databricks’ Private Endpoint works. Everyone optimizes their own part, but the system as a whole remains fragile. ...

Managing data governance on a distributed platform, with hundreds of tables and multiple teams, has never been easy. Anyone working with Azure Databricks is familiar with the problem: tags are applied inconsistently, access policies multiply across each workspace, and ensuring GDPR compliance for sensitive data becomes a manual, error-prone task. I’ve been testing the new Governed Tags and ABAC Policy features of Azure Databricks Unity Catalog over the past few weeks. My conclusion is clear: they bring enormous value to the platform and represent a significant leap forward in the maturity of Databricks governance. ...

Real-Time: Databricks vs Microsoft Fabric or Spark vs Kusto Databricks or Microsoft Fabric for building architectures for (Near) Real-Time data analysis? This is a question that naturally arises when we consider building an architecture for managing streaming data on Microsoft Azure. We are witnessing the evolution of modern data architectures, where we can observe a progressive convergence between batch and streaming paradigms, driven by the need to reduce “Time-to-Insight” and enable instant decision-making processes. In the current landscape, two platforms emerge as undisputed leaders in Azure, offering divergent architectural philosophies. Databricks is based on the Apache Spark engine and Lakehouse architecture, while Microsoft Fabric offers a unified SaaS ecosystem based on multiple specialised engines, including Kusto for Real-Time Intelligence. ...

Milan, December 3, 2025. The atmosphere at WPC always has a special energy, but this year, for me, it felt different. It was my first WPC as a speaker. Getting up on that stage isn’t just about sharing slides, it’s about conveying your vision. Together with my colleague and friend Laura Villa , we accepted an ambitious challenge: to condense into 50 minutes the evolution of a paradigm that is redefining modern business, “Real-Time”. ...