Systems | Development | Analytics | API | Testing

Artificial Intelligence (AI) is primed to reshape the way just about every business operates. Cloudera research projected that more than one third (36%) of organizations in the U.S. are in the early stages of exploring the potential for AI implementation. But even with its rise, AI is still a struggle for some enterprises. AI, and any analytics for that matter, are only as good as the data upon which they are based. And that’s where the rub is.

Because human-machine interaction using natural language is now possible with large language models (LLMs), more data teams and developers can bring AI to their daily workflows.

In this article, we will walk you through the process of implementing fine grained access control for the data governance framework within the Cloudera platform. This will allow a data office to implement access policies over metadata management assets like tags or classifications, business glossaries, and data catalog entities, laying the foundation for comprehensive data access control.

BigQuery now supports generative AI use cases using Gemini 1.0 Pro with Vertex AI, providing higher input/output scale and better result quality.

In my first post of this new blog series, I introduced the concept of “sliding doors” for creating value with data: the divergent paths that organizations can take which can lead to great - or not so great - outcomes. In this post, I want to explore this for what has become a new data imperative for organizations today: effectively reporting their environmental, social and governance (ESG) data to meet new regulations.