Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Cory Benfield discusses the evolution of ...

The acquisition could help enterprises push analytics and AI projects into production faster while acting as the missing autonomy layer that connects Fabric’s recent enhancements into a coherent ...

Microsoft has acquired US-based startup Osmos and will integrate its agentic AI data engineering platform into Microsoft Fabric, enabling organisations to analyse and share data more easily. Osmos ...

Microsoft (MSFT) announced today it has acquired Osmos, an agentic artificial intelligence data engineering platform designed to simplify complex data workflows. No financial details on the ...

Abstract: Data stream learning is an emerging machine learning paradigm designed for environments where data arrive continuously and must be processed in real time. Unlike traditional batch learning, ...

Materials informatics sits at the intersection of experimental science, computation, and data analytics. The aim is simple: use data and models to make discovering, designing, and deploying new ...

Design and implement an end-to-end ETL (Extract, Transform, Load) pipeline using SQL for data extraction and transformation, and Python for orchestration and automation. Use any open dataset (e.g., ...

Millions of users work with SQL to keep the gears of their business turning. In an era marked by relentless digital transformation, the proliferation of AI workloads, and tightening regulatory demands ...

Credit: Image generated by VentureBeat with FLUX-pro-1.1-ultra A quiet revolution is reshaping enterprise data engineering. Python developers are building production data pipelines in minutes using ...

As AI becomes central to the enterprise, data engineers are stepping out from behind the scenes to help shape AI strategy and influence business decisions. In partnership withSnowflake As ...

October 7, 2025—Artificial intelligence (AI) systems rely on data to learn how to perform effectively during deployment. Poor-quality data can cause biased model behavior, unexpected failures, and ...