Systems | Development | Analytics | API | Testing

And instead of getting a generic answer or being told to check your dashboard, the AI pulls the exact numbers from your company’s data and gives you a real answer in seconds. This is no longer science fiction. A new technology called MCP (Model Context Protocol) makes this possible. It’s a standardized way for AI tools to securely connect to your business intelligence and analytics platforms and actually work with your real data.

Conversational Analytics in BigQuery in preview brings a sophisticated AI-powered reasoning engine directly into BigQuery Studio.

In the semiconductor world, “software" is more than just application code. It is a complex stack of firmware, bootloaders, microcode, drivers, and Board Support Packages (BSPs) that are intricately linked to the hardware being designed. To secure the supply chain, meet customer expectations, and maintain market access, semiconductor leaders need a dynamic, "living" SBOM strategy that assesses risk in real-time and provides a single source of truth for all teams to work from.

So as software engineers using AI coding assistants, we’re quickly learning of a new anti-pattern: Hallucinated Success. You give your agent (e.g. Claude via terminal or various IDE code assistants) the command “refactor the billing controller.” The agent happily complies, churning out nice clean code. The agent even goes so far as to write a new unit test suite that passes at 100%. You integrate it. Your test suites pass. Your production code breaks. Why?

Recently, I had the pleasure of sitting down with Cristiano Caetano, VP of Product at Katalon, to explore his journey in software testing. It was a path that began with frustration, evolved through innovation, and now looks toward an exciting AI-driven future. Cristiano's insights shed light not only on his personal career path but also on the broader evolution and future of software testing.

Interactive analytics has a simple promise: answers should show up when people need them, not after the moment has passed. But when usage spikes, many teams end up paying twice: once in latency and again in compute. We recently published a blog introducing Snowflake Interactive Analytics and what it means for ThoughtSpot customers.

It starts innocuously. Marketing signs up for a new analytics platform. IT renews that enterprise license even though only 40% of seats are used. HR adds another recruiter. Travel spending creeps back to pre-pandemic levels despite hybrid work. Then one day: SG&A as percentage of revenue climbed from 22% to 28% over three years. Your margins are compressed. The board is asking questions.

It's 9 PM on Day 5 of the month-end close. Your controller is still at their desk, squinting at the intercompany reconciliation spreadsheet that refuses to balance. The German subsidiary's numbers came in late (again). The currency conversion formulas broke when someone accidentally sorted column B. And tomorrow morning, the CFO needs consolidated financials for the board meeting.

"We were wasting too much time debating the accuracy of numbers as opposed to using that time to make decisions." That's how Satty Saha, Group CEO of CreditInfo-a credit bureau operating across 30+ countries-described the moment he realized his organization had a data problem. Not the kind of data problem you'd expect from a company whose business is data and analytics. An internal data problem.

Executive Summary: As enterprises integrate AI and large language models (LLMs) into their data workflows, the need for predictable, secure, and auditable database interactions has never been greater. Deterministic queries—particularly those encapsulated in stored procedures—provide the guardrails necessary for both human analysts and AI systems to access sensitive data safely.