Systems | Development | Analytics | API | Testing

Introducing the Agentic Semantic Layer: A New Standard for Data Foundations

For data analysts and engineers, the journey from raw data to actionable business insights for business users is never as simple as it sounds. The semantic layer is a critical component in this process, serving as the bridge between complex data sources and the business logic required for informed decision-making. However, not all semantic layers are created equal, and the evolving landscape of AI-powered analytics demands a new approach.

AI initiatives and obstacles: How to stay competitive

By failing to adopt AI and modern data strategies, companies risk falling behind. According to Informa TechTarget’s Enterprise Strategy Group (ESG), 86% of enterprise-class organizations are planning to invest at least $1 million in data and AI initiatives. To help your business keep up, in this video ESG’s Practice Director for Data Management, Analytics & AI, Michael Leone, explores how to build a trusted data foundation, the biggest data challenges faced by companies, and much more.

Tech, talent or trust: What's holding back AI adoption the most?

Tech, talent or trust: What’s holding back organizations from AI adoption the most? Along with exploring that question, in this short video, Practice Director for Data Management, Analytics & AI at Informa TechTarget’s Enterprise Strategy Group, Michael Leone, discusses AI adoption with Hitachi Vantara leaders, including: The video also explores exciting AI use cases, the data issues hindering AI and tips for measuring the success of your AI initiatives.

How to Use Conditional Formatting on Charts in Yellowfin

Welcome to the latest entry in Yellowfin Japan’s long-running ‘How to?’ blog series. This series of blogs aims to provide a step-by-step guide of how to get the most out of your Yellowfin BI experience, such as how to create your own Yellowfin dashboard, and how to use Yellowfin's rich data visualization tools. Catch up on the series below.

The Kafka replicator comparison guide

Let's talk about a problem that might sound simple but gets complex quickly: copying data from one Kafka cluster to another. As our Kafka usage grows, many of us find ourselves managing multiple clusters and needing to share data between them. Or worst still, sharing data to an external cluster. During a London meetup, we explored why this happens, what existing solutions offer, and why we decided to build our own Kafka replicator. Here's what we learned.

Cloudera's AI Studios: Making Advanced AI Accessible to All

The demand for AI-driven applications is surging, and enterprises have reached an inflection point where they can no longer afford fragmented, siloed development. Traditionally, AI development is done by data scientists or machine learning experts with deep expertise in multiple tools and frameworks.