Systems | Development | Analytics | API | Testing

How Nasdaq Architected a $90 Trillion Data Ecosystem

Feb 11, 2026 By ThoughtSpot In ThoughtSpot
Discover how Nasdaq uses data platforms at a massive scale to power markets and prepare for AI. Angie Ruan, Nasdaq’s CTO of Capital Access Platforms, explains how large-scale data systems support market integrity, transparency, and decision-making across public and private markets. She defines what it really means to be AI-ready, how leaders should modernize data platforms, and how market fundamentals help separate real AI value from hype.
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dreamfactory

On-Prem Enterprise Alternatives to Cloud-Hosted AI Dev Tools | DreamFactory

Feb 11, 2026 By Terence Bennett In DreamFactory
This guide explains how enterprises can replace cloud-hosted AI developer tools with secure, on-prem alternatives. It covers architectures, governance, and selection criteria that meet compliance and performance goals. You will learn how teams stand up private code assistants, model gateways, vector search, and policy controls behind the firewall.
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bitrise

You don't have to choose between GitHub and Bitrise

Feb 11, 2026 By Ben Boral In Bitrise
If you're part of a GitHub shop evaluating Bitrise for your mobile app teams, you might be hearing a familiar objection: "Why add another tool? GitHub Actions is our org standard, and it will work for mobile." It's a reasonable point. Nobody wants to maintain a snowflake system that sits outside the approved tool list. But here's the thing — it doesn't have to be GitHub Actions *or* Bitrise. The reality is that mobile CI/CD has unique demands.
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WSO2

What is Semantic Caching?

Feb 11, 2026 By Matt Tanner In WSO2
When we think of a typical API, part of a production-ready setup generally includes a cache. This cache allows for similar requests to be served without having to do the entire roundtrip. But when it comes to AI applications powered by large language models, traditional caching falls short. This is because queries to an AI endpoint may look different in terms of how things are worded or phrased but actually mean the same thing semantically.
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WSO2

Are Your APIs Ready for AI? Preparing Your Landscape for Intelligent Consumption

Feb 11, 2026 By Erik Wilde In WSO2
Getting APIs to work with AI has become one of the major themes in the API space recently. And that’s not surprising because APIs are at the core of an AI’s ability to reach out into the world, to get access to data and information, and to invoke commands and workflows to act. This was always what APIs were for, but in this article we will dive a little deeper what that evolution looks like, and what that means for API governance and management.
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Ep 60 | How AI FOMO and Lost Focus on Culture Harm ROI with Jen Stirrup

Feb 11, 2026 By Cloudera, Inc. In Cloudera
As AI and BI projects race to deliver quick wins, many organizations overlook what truly determines long-term success: the people behind the data. In this episode of The AI Forecast, Jen Stirrup, author, speaker, and founder of Data Relish, joins host Paul Muller to dissect the difference between data literacy (reading the numbers) and data fluency (speaking the language of business). Jen challenges the industry’s obsession with AI FOMO, warning that rushing to deploy models on shaky cultural foundations is the fastest way to derail ROI.
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antino

How to Prioritize AI Investments Using the Impact-Maturity Matrix?

Feb 11, 2026 By Radhakanth Kodukula In Antino
AI is no longer an experimental line item in the budget. For most U.S. CXOs, the real challenge in 2026 is far more practical: where should we place our bets first? With dozens of AI use cases competing for attention, capital, and executive sponsorship, prioritization has become a boardroom conversation, not a lab discussion. Are you investing in AI initiatives that can move the needle this fiscal year, or are you spreading resources thin across pilots that never scale?
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tricentis

Closing AI-generated test gaps with qTest & SeaLights

Feb 10, 2026 By Jason Wides In Tricentis
In today’s fast‑moving software world, release velocity keeps climbing, and AI is accelerating it even further. To keep quality teams aligned with rapid change, we’ve brought together two powerful capabilities: Tricentis SeaLights’ deep code-level insights and Tricentis qTest’s intelligent test management and AI-generated test creation. Here’s how these technologies integrate to create a complete, AI-driven testing feedback loop.
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TestQuality

Scaling Gherkin Software Testing for Modern QA Teams

Feb 10, 2026 By Jose Amoros In TestQuality
Adopting Behavior Driven Development (BDD) starts with enthusiasm. The first fifty scenarios are easy to write. They clarify requirements and align the team. But somewhere around scenario, the reality of Gherkin software testing sets in. Feature files become bloated. Scenarios start to conflict. The "simple" English syntax that was supposed to bridge the gap between business and technical teams becomes a maintenance nightmare that requires constant refactoring.
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