Systems | Development | Analytics | API | Testing

Qlik’s partnership with Amazon Web Services (AWS) is at the very core of our cloud data integration and analytics platform. Think of it like the iconic double helix of DNA. Just as DNA’s intertwined strands carry the fundamental instructions for life, the interconnection between Qlik and AWS carries the innovation and reliability that power the future of enterprise analytics, data integration, and AI.

APIs have become the backbone of modern software architecture—but building and securing them is often harder than it should be. Developers today are expected to expose dozens of data sources as clean, secure, and compliant APIs—fast. From managing authentication to documenting endpoints and enforcing governance, the traditional approach to API integration comes with friction, delay, and technical debt.

Integrating AI into enterprise systems is a high-wire act: you must deliver value quickly—without breaking security, compliance, or scalability. This guide distills security-first patterns CISOs can operationalize immediately: zero-trust for every AI interaction, least-privilege RBAC, end-to-end encryption and secret management, auditable-by-default pipelines, and a platform approach that minimizes custom code and speeds delivery. Bottom line: Treat AI like any external, untrusted client.

As QA teams scale, getting the right data at the right time becomes critical. Yet too often, it’s buried in folders, slowed by clunky filters, or locked behind SQL queries causing wasted time, delayed cycles, and inconsistent reporting. But moving as fast as we are today, you can’t wait on reports or guess where a test case lives. You need answers now. With QMetry’s Smart AI Search and AI-powered SQL generation, QA teams get instant access to data in natural language. No hunting.

Salesforce is the backbone of revenue teams across industries. Yet, the individuals who keep it running smoothly, the Salesforce Admins, rarely receive the recognition they deserve. For years, Admins have been seen as the people who “keep the lights on” by resetting passwords, fixing workflows, and generating reports. In reality, their role has grown into something far more significant. They are the unsung heroes of revenue operations, bridging the gap between data, systems, and strategy.

The healthcare industry is buzzing about FHIR (Fast Healthcare Interoperability Resources). Pronounced “fire,” this widely adopted data standard has been revolutionizing how healthcare information is exchanged. But building a truly modern, secure, and scalable digital health platform takes more than just a data standard. Here comes SMART on FHIR, a framework that builds on FHIR to create a universal, secure, and user-friendly way for applications to access electronic health records(EHRs).

How does your Shopware 6 store’s PHP backend performance compare to other operators of Shopware in general? To answer this question, we have aggregated and anonymized performance data from over 200 Shopware 6 stores over the last quarter and computed benchmark numbers to compare to for the most important page types.

The longer your test cycles run, the slower your product ships. This is one of the core challenges of modern DevOps. Your CI/CD pipeline is fast until it hits the testing stage. That's where parallel testing comes in. Instead of running tests one by one, you can run them side by side. On multiple environments. Across devices. All at once. The result? Faster builds, quicker feedback, and higher deployment confidence.

The test case review process is how we make sure every test case is clear, correct, and ready to run. We treat it as a simple habit that protects quality before execution. When you follow a strong Test Case Review Process, you reduce rework and raise confidence. You also align teams on what matters and how coverage should look. In this guide, we show you how to review test cases effectively with practical steps and examples. We keep it focused, useful, and easy to apply in your next sprint. Let's dive in!

AI coding tools are everywhere; they have changed the way we used to code. These days, people are doing vibe coding with the help of these tools. From suggesting code snippets to explaining errors in plain English, these assistants are becoming as common as Stack Overflow tabs. There are only two names that keep popping up in everyone’s conversation when it comes to AI coding tools: Cursor and GitHub Copilot. But here’s the real question: Cursor vs.