Systems | Development | Analytics | API | Testing

It is Monday morning. Your strongest FP&A analyst opens their laptop and starts pulling the SAP export. They clean it in Excel, cross-reference it against last month's version, and flag the columns that have shifted format again. Tuesday they pull the Dynamics export from the German entity and begin reconciling the two. Wednesday the Polish subsidiary data has not arrived. They chase the local accountant, wait, chase again.

The goal is not complicated to describe. The CEO opens a dashboard, asks a question about regional margin for Q2, drills through to the underlying journal entries, and gets the answer in seconds, without asking anyone. No email to the controller. No waiting until Thursday. No 'let me come back to you on that.' Every CFO who has sat through a pitch for a data platform has been shown this vision. Most of them believe it is achievable. A meaningful number have actually achieved it.

The future of data analytics and AI education has just become even more accessible. The Qlik Academic Program is excited to announce a major upgrade to Qlik Product licenses available for educators and students worldwide. Participants in the program will now receive access to Premium Qlik Cloud Analytics and Premium Qlik Talend Cloud, providing an even more powerful environment for teaching, learning, and innovating with data.

At some point, every real estate or PropTech company hits the same wall: the tools that got you here aren’t the ones that will scale with you. The question isn’t whether to rebuild the stack — it’s knowing what you actually need before you spend money finding out.

TL;DR AI is no longer the future. It is the present. Global enterprise AI spending will roughly reach $2.6 trillion in 2026, generative AI now touches 65% of Fortune 500 workflows, and your competitors in both the mid-market and enterprise space are deploying agents, copilots, and predictive models at a pace that would have seemed impossible 3 years ago.

Static analysis has always excelled at finding defects, vulnerabilities, and compliance violations. Before AI-assisted code remediation, however, developers still had to research the root cause, design a fix, and manually verify that the correction satisfies the relevant requirements. The new, built-in AI-assisted code remediation feature speeds up this process.

Your organization invested heavily in a data warehouse, yet business users still wait days for answers to simple questions. The disconnect between where data lives and who needs it remains one of the persistent challenges in enterprise analytics. With 95% of AI pilots failing due to poor data foundations and accessibility issues, companies need a standardized way to connect AI agents to their existing data infrastructure.

Your data integration team just asked: "Should we use MCP or REST APIs?" The answer is yes to both. With the ETL market reaching $10.24 billion in 2026 and projected to grow to $21.25 billion by 2031, understanding when to leverage each technology determines whether your AI agents can autonomously adapt to changing data needs or require manual code updates for every new integration.

Writing a load test script from scratch is the boring part. You already have the request you want to hammer: it is sitting in your browser’s network tab, in a Postman collection, or described in an OpenAPI spec. So why retype it as a k6 script or build a JMeter test plan by hand? Now you do not have to. LoadFocus converts a cURL command, a HAR file, a Postman collection or an OpenAPI spec into a runnable k6 script or JMeter.jmx test plan.

Smoke testing checks whether a new build is stable enough to test. Sanity testing checks whether a specific fix or change works as expected. Both are quick validation techniques, but they happen at different stages for different reasons. The easiest way to tell them apart: if you just deployed a new build and want to know if core features are still standing, that’s a smoke test.