What is AI NLQ? Understanding AI-Powered Natural Language Query

The rise of natural language query (NLQ) technology in modern business intelligence (BI) and analytics platforms is empowering many companies to streamline data exploration and analysis, and democratize access to insights for more people - not just data experts. But like any technology, the ongoing challenge is to help stakeholders and customers to see the value in using it.

Maximizing GPU Efficiency with ClearML's Unified Memory Technology

AI builders deploying models into production focus on ensuring well-performing models are available for users. Once the model is live, the focus shifts to optimizing GPU usage for efficient deployment. While GPU machines offer the best performance, they are costly to run and frequently remain underutilized.

Hands-on with Apache Iceberg

As organizations move beyond traditional data lakes and warehouses, the need for scalable, performant, and reliable table formats has never been greater. Apache Iceberg is quickly becoming the go-to open-source table format for the modern data stack—bringing the features of SQL tables to the data lake with schema evolution, time travel, partitioning, and ACID transactions. In this exclusive Hevo Learning Lab session, join Alex Merced—Senior Developer Advocate at Dremio and co-author of Apache Iceberg.

Choosing The Right LLM: Why Flexibility Is Key

In this Cortex AI roundtable, Snowflake's Arun Agarwal, Yusuf Ozuysal, and James Cha-Earley highlight why AI model flexibility is crucial for real-world applications. Recognizing that no single large language model excels at everything — coding, reasoning, extraction, or conversation — Snowflake Cortex AI brings the strengths of different LLMs together by integrating leading LLMs like OpenAI, Mistral, Anthropic, and Meta into one secure platform.

Automate data pipelines with BigQuery's new data engineering agent

For years, data teams have relied on the BigQuery platform to power their analytics and unlock critical business insights. But building, managing, and troubleshooting the data pipelines that feed those insights can be a complex, time-consuming process, requiring specialized expertise and a lot of manual effort. Today, we're excited to announce our vision, a major step forward in simplifying and accelerating data engineering with BigQuery data engineering agent.

From Reactive to Orchestrated: Building Real-Time Multi-Agent AI With Confluent

We're entering a new era of artificial intelligence (AI), where intelligence isn't just reactive; it's orchestrated. At Agent Taskflow, we're pioneering a new class of systems: multi-agent orchestration platforms. These systems empower teams of AI agents to coordinate, think, reason, and act in concert—just like human teams. But building these systems at scale requires something most AI platforms overlook: real-time, observable, fault-tolerant communication.

Agentic AI vs Generative AI: Understanding the Key Differences

You’ve probably interacted with AI more times than you can count—whether it’s getting a movie recommendation, using an AI-powered chatbot, or watching AI-generated content. But have you ever stopped to think about how these AI systems actually work? Not all AI is built the same way, and two key paradigms are emerging as game-changers: Agentic AI and Generative AI.