Systems | Development | Analytics | API | Testing

Check out what’s new in ThoughtSpot’s latest release: Spotter learns from your Liveboards and conversations to deliver smarter, more context-aware answers over time. It's now easier than ever to turn complex business questions into trusted, beautifully designed Liveboards with SpotterViz Seamlessly import formulas, measures, and dimensions from Snowflake into ThoughtSpot for a single source of truth across your stack.

AI innovation moves fast, but without compliant data access, even the best ML, AI, and analytics initiatives can stall. In this webinar roundtable, experts from Perforce Delphix, 3T Software Labs, and Redgate explore how organizations can accelerate AI delivery without compromising data privacy, security, or compliance. You’ll hear practical insights and real-world examples on how to remove one of the biggest bottlenecks in modern software and data workflows: access to safe, usable, production-like data.

Visit: speedscale.com/proxymock.

Welcome to Part 7 of the “Data Products for Qlik Analytics” series! In this episode, we take Data Quality to the next level by building advanced data validation rules using IF / THEN / ELSE logic within Qlik Analytics. You’ll learn how to create conditional validation logic that evaluates relationships across multiple columns, enabling smarter and more dynamic data quality checks for your data products.

Understand how to close the gap between AI experimentation and enterprise production. Shub Agarwal, Founder of the AI Trust Lab at USC and author of Successful AI Product Creation: A Nine-Step Framework, shares his AI product management framework for taking enterprise AI strategy from demo to production, drawing on two decades of product leadership at Amazon and Fortune 50 firms. He breaks down why experimentation must tie directly to business OKRs, the four mindset shifts leaders need to scale AI responsibly, and how the AI Trust Lab is building a benchmark evaluation framework for AI model trust and governance.

Kong's AI Gateway applies the same architectural pattern as the API Gateway — now governing LLM, MCP, and agent traffic at the infrastructure layer. Just as API gateways abstracted rate limiting, auth, and caching across microservices, AI gateways do the same for large language models and agents — with token budgets, semantic caching, and semantic routing replacing their REST equivalents. Kong breaks this into three layers: LLM Gateway, MCP Gateway for tool calls, and Agents Gateway for agent-to-agent traffic.#Shorts.

As organizations rush to scale AI, many are learning that better models can’t compensate for weak data foundations. AI hype is everywhere, but operational readiness still isn’t. In this episode of The AI Forecast, Paul Muller sits down with Ravit Jain, founder of The Ravit Show and one of the leading voices in the global data and AI community, to explore the trends shaping the future of enterprise AI.

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.