Systems | Development | Analytics | API | Testing

Dive into the biggest breakthroughs for the Confluent Connect ecosystem in 2025! This year, we made moving data easier than ever, from modernizing legacy systems with the Oracle XStream CDC Premium Connector to empowering developers with Custom SMTs and Custom Connectors on Google Cloud. Discover the over 10 new connectors we launched, including Snowflake Source, Azure Cosmos DB v2, and Neo4j Sink, plus the release of Confluent Hub 2.0. Learn how Confluent Cloud connectors are breaking down silos and building bridges for your next-gen AI and data modernization projects.

In this episode of The AI Forecast, Anu Jain, founder and CEO of Nexus Cognitive, joins host Paul Muller to introduce a transformative idea: AI doesn’t have a last mile problem. It has a first mile problem. While AI models and algorithms can scale instantly through the cloud, their success still depends on the quality, provenance, and readiness of the data that feeds them.

Just like humans, AI systems are shaped by how they are brought up. In the case of Large Language Models, this upbringing happens through data collection, training, and productization. At each of these stages, bias can quietly enter the system through the data we select, the way models are trained, or the assumptions embedded into the final product. These biases, whether intentional or accidental, influence how models think, respond, and interact with users in the real world.

Message brokers are a critical component of modern distributed systems, facilitating asynchronous communication between services. Load testing message broker integrations requires special considerations since the interaction patterns differ from traditional HTTP-based APIs. Speedscale provides specialized tooling to help you load test applications that integrate with message brokers by.

In machine learning, an 85% accurate model is often considered a success because we accept that data is messy, the real world is unpredictable, and chasing perfection is rarely worth the cost. However, in software testing, especially in the GenAI era, the question of “Can we test 100%?” still comes up. With AI systems producing probabilistic outputs where the same input can lead to different results, absolute coverage is unrealistic. Confidence scores already tell us that uncertainty is part of the system, and testing needs to acknowledge that reality.