Systems | Development | Analytics | API | Testing

Learn what Debezium is, how its CDC architecture and connectors support MySQL and SQL Server, and which of the three deployment modes fits your pipeline.

A customer emailed on a Tuesday: checkout hung for ten seconds. I opened our tracing tool, punched in the time window, and got nothing. The trace was sampled out. We keep 1% of traces, like most shops with real traffic do. The one request that actually mattered was in the 99% we threw away. I spent twenty minutes admiring our observability stack before admitting it couldn’t answer a first-grader’s question: what happened to this person? Here’s what I know now.

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.

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.

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.

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.

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.

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.

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.

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.