Systems | Development | Analytics | API | Testing

Why Apache Iceberg & Open Lakehouse is the Foundation for Data & AI Workloads

In this discussion, Dipankar - Cloudera’s Director of Developer Relations sits with Navita - Director of Product Marketing to unpack why Apache Iceberg has emerged as the foundation of the open lakehouse - and why it’s increasingly essential for modern Data & AI workloads. Dipankar & Navita walks through how Iceberg became the de facto standard among open table formats, what it's design enables (interoperability, engine-agnostic access, reliable metadata), and why openness matters as organizations move toward multi-engine, hybrid data architectures.

Why You Should Run AI-Generated Code in a Sandbox

At their best, code generation LLMs reduce cognitive load, accelerate iteration, and serve as a great pair programmer for well-scoped tasks. That said, they also introduce a level of risk. Whether it’s using a variable that was never declared, making up functions that aren’t part of a class, using code from outdated packages, or misdiagnosing an issue, code generation models can create problems.

Meet the New BI A-Team

Talk to anyone who works with data, and you’ll hear a familiar story: Data engineers are still bogged down cleaning, prepping, and untangling semantic models. Analysts are churning out dashboard after dashboard, with little time left for real analysis. Developers are hand-coding embedded analytics, turning every new feature into a months-long project. And business users are stuck in line, waiting for answers.

How SpotterCode Supercharges ThoughtSpot Embeds for Developers

Developers, ditch the documentation dive! SpotterCode is your 10x coding partner for embedded intelligence. ThoughtSpot's Nicolas Rentz shows how SpotterCode leverages the ThoughtSpot SDK docs and code examples to auto-generate production-ready code directly in your IDE. Accelerate your path to market with clean, seamless integration.

2026 Data & AI Predictions: What Trends Will Shape the Future?

We recently released our 2026 Confluent Predictions Report, outlining bold ideas and trends that are shaping the future of data, AI, and real-time systems. And stay tuned for an upcoming episode of the Life Is But a Stream web show that will air early in the new year. Join the conversation as host Joseph Morais sits down with Sanjeev Mohan, independent analyst at SanjMo, for an exciting roundtable discussion breaking down those predictions. Are they forecasts? Are they trends? And which ones will matter most as we move forward into 2026?

Move More Agentic Workloads to Production with AI Gateway 3.13

Kong AI Gateway 3.13 moves enterprises from AI experimentation to shipping production-grade agents by unlocking new capabilities focused on agentic security, developer productivity, and resilience, including MCP tool-level access control, expanded provider support, and smarter load balancing.

The Age of AI Connectivity

Kong was born to connect. The world is shifting from connecting cloud services with apps to connecting LLMs through agents. API calls and tokens are moving in tandem; a new unit of intelligence is forming. As AI traffic explodes into hypervolumes, speed is all that matters. The same principles of performance, security, and reliability behind Kong are essential in an agentic world. A new connectivity layer for AI is born.

Operationalizing Agentic AI with Hitachi iQ Studio and NVIDIA Nemotron 3

NVIDIA just announced NVIDIA Nemotron 3, a new family of open models, datasets, and libraries designed to support long-context reasoning and multi-step AI workflows. With the ability to work across enterprise ecosystems, this family of models empowers enterprises to build and deploy reliable multi-agent systems at scale, offering an important set of technologies at a pivotal moment in AI evolution.

7 RAG Evaluation Tools You Must Know

RAG evaluation measures how effectively a system retrieves relevant context and uses it to generate grounded answers. These evaluations detect hallucinations, measure retrieval precision and reveal where pipelines degrade after model updates or knowledge-base changes. Engineers rely on these tools to maintain output quality, prevent regressions, validate prompt and architecture choices and ensure that production answers stay aligned with trusted sources.

Replit vs Cursor : Which AI Coding Platform Should Developers Choose?

In an age where software developers are speeding up their code development to meet the demand of rapid application deployment, there are new tools being developed based on Artificial Intelligence (AI) technology. Replit and Cursor have received a lot of excitement for both of these platforms due to their use of artificial intelligence in assisting developers with coding.