The rise of Large Language Models (LLMs) has ushered in an era of AI-driven transformation, with AI agents at the forefront, promising to automate complex… Read More »Optimizing Agentic Workflows: The Power of Fine-Tuning and “Focused” LLMs
In today’s rapidly evolving technological landscape, the role of a data professional is undergoing a profound transformation. As businesses increasingly leverage AI and intelligent systems,… Read More »From Data Engineer to Data Lead: Guiding the Future of Intelligent Data
The promise of Artificial Intelligence (AI) to revolutionize business operations, enhance decision-making, and unlock new revenue streams is no longer a distant dream—it’s a present… Read More »Beyond the Hype: Practical Strategies for Building AI-Ready Data Pipelines
Discover how autonomous AI agents are revolutionizing data analytics by understanding organizational knowledge and enabling natural language data interactions for unprecedented productivity gains
Successful metadata management ensures data quality, improves discoverability, enhances governance and enables business users and data teams to derive value from enterprise data assets efficiently. In this article I’ll lay out a plan and guidelines for building the metadata management plan and strategy to help you succeed in 2025 and going forward.
Two roles that are often confused, conflated, or outright merged are the Data Engineer and the Data Platform Engineer. On the surface, they might seem interchangeable, but the nuances matter, especially when you’re managing teams or straddling both roles in one job.