George Fraser: AI is a genuine supply side breakthrough for analytics
Fivetran + dbt Labs CEO George Fraser, President Tristan Handy, and Andreessen Horowitz general partner Martin Casado talk about Open Data Infrastructure in the AI era.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Cautionary Tales from the Age of AI
Can AI agents actually fix production Kubernetes clusters, or are we just trapped in flashy AI marketing hype? In this Cloud Native Budapest session at Cloudera, Yurii Palamarchuk (Cloudera) breaks down why most AI demos fail in production, and Péter Megyesi shares how autonomous AI agents (Claude Code, kubectl) resolved a complex 6-year-old Ceph & Kubernetes outage.
Why Enterprise AI Context Lives in People's Heads, and How to Fix It
Why does context always seem to live in people's heads in enterprise organizations? According to Jessica Talisman, author of The Ontology Pipeline and founder of Contextually LLM, the answer is that organizations have invested heavily in data management for decades — and almost nothing in knowledge management. In this clip from Day 1 of our Semantic & Context Layer Series, Jessica joins Jane Smith and Sonny Rivera to untangle the terminology confusion around semantic layers, context layers, and memory and explain why the gap between data and knowledge is exactly where AI answers break down.
Open Data Infrastructure in the age of AI agents
Fivetran + dbt Labs CEO George Fraser and Fivetran + dbt Labs President Tristan Handy sit down with Andreessen Horowitz general partner Martin Casado, one of the leading investors in software and AI infrastructure, to talk about Open Data Infrastructure in the AI era. Martin and his team have invested in dbt, Fivetran, Tabular, Cursor, OpenRouter, and many other companies.
Appian Q3 Release Highlights: AI Speed Meets Enterprise Controls
AI is evolving faster than organizations can keep up, and often it’s the most important operations that fall the furthest behind. That’s exactly why Appian is focused on AI for complex, enterprise-grade processes—to make applying AI automation to important work easy. Over the last quarter, Appian has continued to invest in capabilities that help you deploy AI fast without sacrificing control: Over 80% of developers regularly use AI coding tools.
Optimize Load Test Script Maintenance with AI
AI-driven load test script maintenance can significantly reduce the manual updates required by traditional tools. As your application evolves – whether through new user flows, updated endpoints, or changing parameters – AI features in some platforms help automatically adjust scripts to reflect these changes. This simplifies maintenance and allows your team to focus on testing outcomes rather than constant script rewrites.
Marketing Root Cause Analysis: Using AI to Diagnose Why Performance Dropped (and What It Leaves Out)
Your dashboard already told you ROAS dropped. The expensive question is why, and answering it means correlating data across platforms, isolating variables, and testing hypotheses against the numbers. Done by hand, that is an afternoon per incident. Done by AI, it takes seconds, with one catch.
Top Automated Testing Platforms for AI-Driven Software Development
AI-driven software development has reshaped what QA teams face daily. Codebases evolve faster, release cycles are shorter, and a single missed regression can hit production within hours. Today's top automated testing platforms have moved well past scripted checks, they now use machine learning to write, maintain, and adapt tests on their own. The five platforms below represent where the market stands in 2026, chosen for their AI architecture, enterprise track record, and real documented results.
Bazel Remote Build Execution: run your builds in parallel on Linux and macOS workers
With Remote Build Execution (RBE) your Bazel build and test compilations run in parallel across many remote workers at once, instead of in series, one by one on a single machine.
What Is a Control Plane for Enterprise AI? A Look Inside Klique
A control plane for enterprise AI is the layer between the people and agents using AI and the infrastructure that runs it, meaning your models, GPUs, clusters, and cloud accounts. It decides who can use what, sends each request to the right resource, and records what that request cost. Klique is the control plane for enterprise AI. Teams get self-service access to approved tools, models, and GPUs, and IT sets the policies, quotas, and budgets behind them.