Systems | Development | Analytics | API | Testing

AI Inference for Mission-Critical Applications | Run AI Where Your Data Lives

What happens when your AI system stops responding in the middle of a critical decision? This demo shows how organizations run AI inference for real-world applications like pneumonia detection to: See how Cloudera AI Inference Service enables teams deploy and monitor multiple models with full control, predictable costs, and no dependency on external APIs, so mission-critical AI keeps working when it matters most.

Feed Your Data Lake With Real-Time, Analytics-Ready Tables for 30-50% Lower Cost Using Tableflow

Organizations are under pressure to feed data lakes and lakehouses with fresher data while keeping a tight lid on cloud spend. The problem is that most ingestion stacks weren’t designed for the real-time, high-volume workloads that power modern analytics and artificial intelligence (AI). They rely on layers of connectors, ETL jobs, and maintenance processes that quietly inflate both infrastructure and operational costs. Confluent’s Tableflow was built to change that equation.

More Signal, Less Guesswork: New Kafka Observability Updates in Confluent Cloud

We’re introducing enhanced visibility for streaming workload performance on Confluent Cloud, making it easier for developers and operators to understand, troubleshoot, and optimize real-time applications. As Apache Kafka has become the backbone of data streaming, many teams rely on Confluent Cloud for its scale, elasticity, and reduced operational burden.

How to Prevent AI Hallucinations: 3 Hidden Threats When AI Analyzes Your Data

A VP of Marketing presents an AI-generated performance review on a Monday morning. The CAC numbers are clean. The trend lines are directional. The exec summary recommends a $200K budget reallocation from paid search to organic content. The CFO nods. The budget shift is approved before lunch. Two weeks later, an analyst spot-checks one figure against the source system. The number doesn’t exist anywhere in the connected data.

How to Sync Semantic Models Between ThoughtSpot and Snowflake with Cortex Code

Migrating semantic models between ThoughtSpot and Snowflake just got significantly faster. Our Senior Product Manager, Damian Waldron, walks through how to use ThoughtSpot's Agent Skills in Snowflake Cortex Code (CoCo) to migrate and sync between ThoughtSpot Models and Snowflake Semantic Views, including complex schemas with fan traps, semi-additive measures, and shared dimensions. In this video you’ll learn how to.