Systems | Development | Analytics | API | Testing

Even if you can see exactly what is wrong with your data platform. Why is none of it getting fixed? A 30-minute conversation on closing the gap between what your dashboards see and what your team can actually get done, across Databricks, Snowflake, and BigQuery. Why it matters Most data teams are not short on insight anymore. The dashboards are full. Cost reports flag cost overruns. Observability platforms catch infrastructure misallocations. New AI assistants will even draft query rewrites for you.

Every product team wants smarter analytics and AI. But for organizations that operate on-premises, in a hybrid-cloud environment, or regulated industries that can’t share protected data on the cloud, this forces them to either migrate or leave intelligence features behind. Can you access advanced analytics features without being boxed into a vendor’s cloud environment? Watch our video to learn: How to access analytics without compromise About tailored analytics experiences in a single platform.

Most teams building agents with Temporal have solved the backend problem: crashed workflows restart, LLM call failures retry automatically, and long-running tasks complete reliably. What they haven't solved is the client side -- what happens to the stream when the user's connection drops, when they switch devices, or when two sub-agents are working concurrently and the client needs a single coherent view.

They fit into the workflows, processes, and environments teams already have. As Chris Armstrong, Manager of Developer Relations at SmartBear, explains, every organization is on a different stage of its journey. Some are exploring AI. Others are scaling it. Many are managing a mix of legacy systems, modern platforms, and everything in between. What teams need isn't another platform that demands a complete overhaul. They need solutions that respect their context while helping them move forward with confidence.

Hey, did you know? The fastest way to AI might not be adding more tools. It might be getting more value from the data you already have. See how Cloudera helps reduce data costs, unify governance, and make enterprise data AI-ready without disrupting operations.

For decades, network teams have been forced to choose between speed and stability. AI may finally be changing that equation. In this episode of The AI Forecast, Paul Muller sits down with John Capobianco, Head of AI and Developer Relations at Itential and author of “Automate Your Network,” to explore how AI is reshaping the future of network operations. Drawing on decades of experience in network engineering, John explains why network automation has struggled to gain traction and how AI, agents, and Model Context Protocol (MCP) could finally break the bottleneck.

Stop letting broken downstream dependencies kill your workflow. Use proxymock to record backend data and inbound calls, then flip on a mock whenever a system goes offline. You can even replay traffic to test your code changes and ensure your endpoints are still working perfectly.

Click the link below to mock your backend dependencies in under 2 minutes.

When AI Starts Shipping Code: Why Control Is Your New Competitive Advantage AI systems are already generating code, creating tests, and orchestrating infrastructure changes across enterprise software delivery. But faster execution without control creates a new kind of risk. In this webinar, Perforce CTO leaders explore why uncontrolled AI execution is emerging as one of the biggest operational risks in modern software delivery — and what leading enterprises are doing about it.

What's driving automotive software development in 2026? Discover key trends, market challenges, and modernization strategies with highlights from our annual report.