Systems | Development | Analytics | API | Testing

BigQuery gen AI functions AI.GENERATE and AI.GENERATE_TABLE are now GA, and can be used with the new AI.EMBED and AI.SIMILARITY.

If you have ever asked three teams for the definition of the “same” metric and gotten three different answers, you have already met one of the most expensive, least talked about problems in modern data. It is not a lack of data. It is a lack of shared meaning. As analytics and AI spread across more tools, clouds, and teams, business context often fails to travel with the data. A metric defined one way in a dashboard gets redefined in a notebook.

Fee transparency in rental listings is no longer a design or disclosure detail, but a regulatory and commercial requirement for real estate and PropTech platforms. Recent FTC enforcement actions and accelerating state legislation now say that pricing logic must be explainable, consistent, and defensible across every channel where rent is shown. Real estate companies using listing platforms, PMS tools, marketplaces, or leasing experiences now need to update their sites and comply with the new regulation.

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The foundation of every successful customer relationship is trust. At Confluent, we understand that for our customers and prospects to innovate with confidence, they must have complete trust in the security and integrity of our platform. Our commitment goes beyond simply providing a secure product. It’s about empowering our customers with the tools and transparency they need to feel confident in their data streaming architectures.

Shipping fast feels great – until something breaks in production. Sometimes, even solid-looking builds fail just because one small issue slipped through testing. That’s where software quality gates step in. They act as automated checks that stop risky code before it moves ahead in the pipeline. Rather than relying upon instinct, we rely on data – code coverage numbers, test results, and security signals.

When a user asks your AI assistant a question, who actually runs the database query? In most enterprise AI deployments, the answer is troubling: a shared service account with broad access to everything. The user's identity evaporates the moment their request enters the AI system. This architectural pattern creates security gaps, compliance failures, and data leakage risks that undermine enterprise AI adoption.

AI is changing how work gets done. But for many data leaders, it’s also creating a new challenge: managing the cloud bill. As more people (and more AI agents) query data, cloud data warehouse (CDW) spend can spike fast. Costs become harder to predict, and teams end up making tradeoffs—scaling AI insights or staying within budget. That tension creates a real bottleneck on the path to becoming AI-ready.

Our goal with the Tricentis blog is to distill insights that help QA professionals navigate the massive, AI-driven transformation happening across the software delivery landscape. To that end, I reached out to experts across Tricentis, from product and services to marketing and strategy, to hear what they’re really thinking about AI in QA right now. This group brings decades of experience building testing products, guiding enterprise transformations, and shaping how organizations adopt AI.

70% of teams are already integrating generative AI tools into their daily workflows, according to our 2025 State of Game Technology Report. Now more than ever, teams are looking to connect their AI tools to the services and applications they rely on to get work done. To address this issue, the industry has begun to standardize using the Model Context Protocol (MCP) to connect their existing tools and LLMs like Claude, GPT, and Gemini.