Monitoring, Audit Trails, and Compliance with ClearML

The previous posts in this series built the security model layer by layer: identity, configuration governance, service account automation, compute policies, and production model serving. This final post covers what holds all of it together: the monitoring and audit layer that records every action, every API call, and every resource event and makes the full picture visible to the people responsible for it. It accompanies our Enterprise AI Infrastructure Security YouTube series.

How to Use Snowflake Semantic Views in ThoughtSpot

Learn how to go from Snowflake Semantic View to a fully functional ThoughtSpot Liveboard in under five minutes. Our Senior Director of Product Management, Antonio Scaramuzzino, shows the powerful native integration between Snowflake Semantic Views and ThoughtSpot’s Spotter Semantics. You’ll learn how to: + Skip the manual mapping. Use the Semantic Views you’ve created in Snowflake directly in ThoughtSpot.

How Wix's AI Agents Stay Ahead of the Rest | Life Is But A Stream

Real-time data and AI are converging—and companies that have already solved the data pipeline problem are pulling ahead fast. Wix processes over 40 billion interactions every day across hundreds of millions of websites, and the architecture behind that scale didn't happen by accident. It was built, lane by lane, around the principle that your upstream data must be at least as fast as your fastest use case.

You're not doing AI transformation. You're doing AI decoration.

Every enterprise AI story right now follows the same plot. You pick a system — Salesforce, Workday, SAP, NetSuite — and you bolt an AI agent on top of it. The agent can summarize deals. It can write follow-up emails. It can pull a report without you clicking through five dashboards. It is genuinely useful. And it is not transformation. What you have built is a smarter interface on top of a system designed for humans.

Why Your AI Strategy is Breaking: The Power of AI Anywhere with Cloudera

Hey, did you know AI can’t be confined to just one environment? AI is moving faster than ever, but it cannot be confined to a single environment. From the public cloud to on-prem data centers and out to the edge, AI is everywhere. However, when these environments remain siloed, your data strategy breaks—leading to inconsistent governance and scaling roadblocks. In this video, discover the vision of AI Anywhere. To unlock real business value, AI must operate exactly where your data lives, maintaining the same level of control, trust, and security across every platform.

Lenses MCP Server with OAuth 2.1

You can now drive Lenses from Cursor, VS Code, IBM Bob or Claude Code without running any extra piece of infrastructure locally. Lenses MCP offers secure tools across topics, schemas, Kafka Connect, SQL processors, consumer groups, datasets and pod logs: everything an engineer would normally click through in the Lenses UI, now reachable from any MCP-compatible client over HTTP.

Introducing Kafka Skills for AI Engineering Agents

If you've written a line of code in the last 18 months, you already know this. Tools like Claude, Codex, Bob, Kiro and Cursor have made agentic software engineering the default. Most developers today are writing prompts as much as they are writing code. That shift changes what ‘developer experience’ means. Clean UIs, useful tools and good docs are still the foundation but the focus has shifted to ensuring a coding agent actually knows what it is doing, in the hands of a developer.

Scaling Embedded Analytics Across Customers: A Practical Blueprint

Embedded analytics is no longer a nice extra. It now shapes revenue, retention, and the customer experience. A few charts in one customer portal can look fine. The same setup starts to crack when it serves hundreds of tenants, each with different data, access rules, and branding. That is the core shift. Teams move from one-off embeds to a product layer that must run across many customer environments. The work is not just visual. It touches latency, isolation, governance, and cost control.

3 ways Fivetran uses AI internally

Like many companies, Fivetran has recently piloted a number of projects and internal tools using AI. AI offers the potential to augment or accelerate a huge range of day-to-day business tasks, including better understanding and prioritizing customer requests, surfacing trends across multiple business units, and automating customer communications. The key to delivering value using AI is to ensure that it has access to quality data and a data infrastructure capable of providing it.