Systems | Development | Analytics | API | Testing

Teams use different tools in their software testing life cycle. The problem? Each tool has its own way of communicating. The MCP (Model Context Protocol) Server is a new approach to integrating these tools. It’s like a universal translator, so your testing tools, scripts, and AI copilots can share context without endless plugins or one-off integrations.

Clicking links is one of the most basic things a user can do on a website. If you're writing test scripts, it’s also one of the most common actions you’ll need to automate. With Selenium WebDriver, you can interact with any hyperlink on the page, no matter if it's visible, hidden, dynamic, or opens in a new tab. All you need is the right locator and a clear understanding of how the browser responds.

When your application crashes during peak traffic or returns cryptic error messages to frustrated users, the culprit is often hiding in plain sight: untested APIs. According to recent industry data, APIs now account for 71% of all internet traffic, yet many businesses still treat API testing as an afterthought.

Fivetran is recognized as a Leader in Data Integration & Modeling in Snowflake’s Modern Marketing Data Stack 2026 report.

Imagine that you need to know the exact location of the train that you’re about to catch. Trains can be equipped with IoT sensors that can publish location coordinates via Kafka streams in real time. You will be using a mobile application to view a train’s location. Mobile applications and web applications are compatible with WebSocket, a web-friendly protocol, but the location data is received via a Kafka stream. How do we convert the real-time Kafka feed into a real-time WebSocket feed?

Did you know that poor data quality costs the UK economy an estimated £244 billion annually, and up to 75% of ERP projects fail to deliver due to data issues? In today’s data-driven world, enterprise resource planning (ERP) systems promise to streamline operations, boost visibility, and sharpen decision-making across sectors such as banking, retail, and manufacturing. Yet, many UK organisations pour millions into these platforms only to see them falter. The culprit? Overlooking data assurance.

By the year 2025, simply writing clean code doesn’t guarantee a flawless mobile application; ensuring your app works across a jungle of devices is the key to success. The challenge stems from mobile device fragmentation, the unlimited, confusing combinations of operating systems, display sizes, and hardware specifications that make testing a moving target.

To use synthetic test data or to use test data masking — that is the question. But the answer may not be what you expect. Before we dive into that, what’s happening in today’s business landscape that’s prompting the question around synthetic vs. masking? Delivering high-quality applications at lightning speed is expected in today’s CI/CD world. Fast time-to-market is at odds with security and compliance requirements.

Yes, you’ve heard it all before: the frequency of cyberattacks and their devastating aftermath, organizations’ gaps in protecting sensitive data, and the financial consequences of not complying with GDPR and the likes. I am not here to share any old news. But there is a risk that is not discussed frequently enough in the news. And it should be. How often do you suppose data in non-production environments is compromised or fails compliance audits?

Whether it’s financial transactions being processed in milliseconds, customer interactions powering personalized experiences, or machine learning models making predictions, the quality of your data directly shapes the quality of business outcomes today. Put simply: bad data equals bad decisions. The costs aren’t just theoretical—they show up as inaccurate dashboards, failed compliance audits, customer churn, and wasted operational resources.