Systems | Development | Analytics | API | Testing

SpotCache: Scale AI-ready data without cloud-spend surprises

AI is changing how work gets done. But for many data leaders, it’s also creating a new challenge: managing the cloud bill. As more people (and more AI agents) query data, cloud data warehouse (CDW) spend can spike fast. Costs become harder to predict, and teams end up making tradeoffs—scaling AI insights or staying within budget. That tension creates a real bottleneck on the path to becoming AI-ready.

2026 Predictions: What's Next for Data Streaming and AI | Life Is But A Stream

AI isn’t just evolving—it’s reshaping who your customers are, how systems operate, and what real time really means. From machines making purchase decisions to agents increasing query volume across databases, the realities of 2026 are forcing leaders to rethink data architecture and governance strategies at a fundamental level. In this episode, Joseph is joined by Will LaForest (Field CTO, Confluent), Adi Polak (Director of Developer Advocacy & Experience, Confluent), and independent analyst, Sanjeev Mohan, to break down critical insights from Confluent’s 2026 Predictions Report.

Empowering Customers: The Role of Confluent's Trust Center

The foundation of every successful customer relationship is trust. At Confluent, we understand that for our customers and prospects to innovate with confidence, they must have complete trust in the security and integrity of our platform. Our commitment goes beyond simply providing a secure product. It’s about empowering our customers with the tools and transparency they need to feel confident in their data streaming architectures.

Qlik Joins Snowflake-Led Open Semantic Interchange to Bring Consistent Business Meaning to Analytics and AI

If you have ever asked three teams for the definition of the “same” metric and gotten three different answers, you have already met one of the most expensive, least talked about problems in modern data. It is not a lack of data. It is a lack of shared meaning. As analytics and AI spread across more tools, clouds, and teams, business context often fails to travel with the data. A metric defined one way in a dashboard gets redefined in a notebook.

Chat with Your Data: The Official Databox MCP

Your AI is brilliant, but it’s blind. Until now. We are thrilled to launch the official Databox MCP (Model Context Protocol). This open standard server bridges the gap between your business data and your favorite AI tools, turning general-purpose LLMs into specialized data analysts that know your business data. Stop manually exporting CSVs or taking screenshots of dashboards. With Databox MCP, you can connect 130+ data sources (Google Analytics, HubSpot, Salesforce, Stripe, and more) directly to tools like Claude, ChatGPT, Cursor, and n8n.

Multi-Node Training with ClearML

Orchestrating distributed AI workloads Distributed (multi-node) training has become a requirement rather than an optimization for many modern AI workloads. As model sizes grow, datasets expand, and training timelines tighten, teams increasingly rely on multiple machines, often with multiple GPUs each, to complete training efficiently.

Sales Leaders: Turn Intuition into Impact #OnTheSpot with Spotter

Sales Leaders: Are you making decisions based on data, or just a "gut feeling"? If you want to move faster, you need to see this. James Smith, our SVP of EMEA, is demonstrating a hashtag#wowmoment that turns a vague intuition on SDR pipeline progression into a multi-million dollar revenue roadmap using Spotter.