Systems | Development | Analytics | API | Testing

Modern API architectures are no longer linear. A single request can traverse multiple layers of authentication, transformation, enrichment, and observability. As these flows grow more dynamic, the need for fine-grained control over when plugins execute becomes critical. For years, the standard approach to API Gateway configuration followed a strict hierarchical model: you applied a plugin to a Service, a Route, or a Consumer.

How Kong Gateway 3.14 closes the consistency gap in IAM-based authentication across AWS, Azure and GCP — and what it means for your production deployments Enterprise security teams have clear requirements: no static credentials, no exceptions. Every service-to-service connection, whether it's Kong talking to databases, caches, or vaults, should authenticate using the same IAM-based identity model that governs the rest of their cloud infrastructure.

How OAuth 2.0 Token Exchange Reshapes Trust Between Services — and Why the API Gateway Is Exactly the Right Place to Enforce It OAuth 2.0 Token Exchange fundamentally alters how services establish trust. And the API gateway is the ideal location to enforce this new model. Modern applications don’t run as a single monolithic.

BigQuery Graph lets data professionals model, analyze and visualize massive-scale relationships in an entirely new way.

Learn how AI is revolutionizing data integration. Explore key AI data processing benefits, techniques, and top AI data integration tools.

With the relative newness of the iPaaS (Integration Platform as a Service) vertical, no single platform has emerged as the de facto choice for enterprises. This can present a challenge if you're trying to choose among the large pool of solutions available – especially when certain solutions labeled "iPaaS" serve very different use-cases.

“The data integration landscape is currently undergoing a seismic shift. We are moving away from an era of human-led data pipelines and entering the age of Agentic Data Management.

At Qlik Connect, one of the big messages we’re putting in front of customers is this: you’re closer to agentic AI than you think. I believe that because a lot of our customers already have more of the foundation in place than they may realize. If you have been working to improve data quality, strengthen governance, connect data across the business, and move analytics beyond reporting into real decision support, you are already building the conditions agentic AI needs to deliver value.

Picture this: it's the Thursday before a major release. The VP of Engineering asks a simple question in the planning meeting: "Are we confident we can ship Friday?" The QA lead opens four dashboards, pulls an export from the test management tool, cross-references it with execution results from a separate environment, reconciles defect counts in the bug tracker, and 40 minutes later delivers a hand-built status summary that is already slightly out of date. The team isn't slow. The team isn't incompetent.