Systems | Development | Analytics | API | Testing

Tableau's direction is clear. For embedded analytics teams serving customers with strict governance, data residency, or infrastructure requirements, "cloud only" constrains your product, your market, and your roadmap. In this video, we break down what Tableau Next's Hyperforce launch actually means for ISVs and SaaS vendors building embedded analytics, including: If you're doing real long-range planning, this is the conversation that matters.

Learn how to generate a client SDK for a production service when you have no documentation, no OpenAPI spec, and no remaining team knowledge of the original Ruby code. This demo shows you how to capture real production data from a running app and transform it into a functional Java client library in minutes. Visit proxymock.io OR speedscale.com to learn more.

Most AI revenue will flow through tokens — and the two bottlenecks are tokens per watt (energy cost) and tokens per second (throughput). Tokens per watt determines how much output you can generate from a fixed energy supply — already constrained and getting tighter. Tokens per second sets the ceiling on how fast that revenue can flow. Kong's AI Gateway optimizes both at the connectivity layer: semantic caching and semantic routing increase token output without adding watts or latency.#Shorts.

The very system designed to keep you comfortable is the same one keeping you trapped. You pay only for rows that change. Simple, right? But the reality is quite different. MAR mechanics are designed to blow up when you scale. You expect to be paying for real data changes but you end up paying for the whole row, regardless of how little data you move on it. It lures you in with simplicity but the underlying mechanics is an alternate reality. Every connection follows its own pricing curve, the costs stop behaving logically, and forecasting your bill turns into a nightmare.

Zscaler's Tiffany Blakeney shares how her team replaced fragmented tools and months-long development cycles with Snowflake's all-in-one AI platform. By consolidating all data, APIs, and AI models in one secure platform, Zscaler reduced campaign creation from months to minutes—and more importantly, gained the trustworthy, governed AI foundation a cybersecurity company demands. Learn how they're using Snowflake's integrated AI capabilities to move from POC to production faster than ever while maintaining the security posture critical to their industry.

Get a quick walkthrough of qTest Manager by Tricentis—the test management platform built for modern QA teams and developers. In this video, you'll see how qTest Manager is structured around four core components: Test Plan – Set up and organize your projects with timelines, releases, and version tracking Requirements – Manage and track requirements directly within your QA workflow Test Design – Build and organize your test case library.