Systems | Development | Analytics | API | Testing

Picture this: You’re creating test cases for a new feature. You have a Jira ticket with text requirements, a Figma mockup from design, a workflow diagram from the architect, and a screenshot from a stakeholder meeting. Traditionally, you’d manually translate all of this into test steps: describing the UI in words, interpreting the diagram, cross-referencing the mockup. But what if your testing tool could “see and “understand” all these artifacts directly, just like you do?

Today marks a monumental step in our commitment to fueling the growth, reach, and impact of our global partner network. We’re thrilled to announce the official launch of Confluent Marketplace (formerly Confluent Hub), a centralized resource designed to accelerate innovation, drive connectivity, and dramatically simplify the developer experience within the data streaming landscape. For years, integration engineers have been the quiet force behind the modern digital world.

You can make your API observability posture more powerful and beneficial by treating Moesif as an engineering implement. The platform automatically captures API traffic out-of-the-box and provides actionable analytics and visualizations. However, the degrees to which they precisely and empirically illustrate the data, depend on where and how you’ve integrated Moesif.

API and AI services now sit at the heart of modern products. However, the more we use them, the harder it seems to become to account for the budget. Launching an AI product often leads to massive end-of-period bills. This requires attributing costs to the key internal power users and consumption drivers. The challenge is identifying the departments, products, or projects responsible for the consumption, and the extent to which they contribute.

Picture this: It's 3 AM. You’re on-duty in case there is an outage. A team in the other part of the world merged PR and released a new version of K2K Replicator and it crashed. Consumer group lag is spiking to the universe. You’re paged & woken up, went to your laptop, the team already reverted PR, things are stabilising, but what really happened, you have to investigate now as postmortem has to be done.

In the last ten years, software testing has advanced significantly, but today’s applications require more than just using conventional forms of automated software testing or entry-level tools that employ artificial intelligence (AI). The rise of microservice architectures, API calls, and continuous deployment has led to another category of software testing products called "Generative" AI Testing tools.

Earlier this year at Qlik Connect, we introduced Qlik Open Lakehouse, a fully managed, Apache Iceberg–based platform designed to make it easy and cost-effective for organizations to ingest, optimize, and manage data in open lakehouse architectures. And the first version of Qlik Open Lakehouse is generally available as of Sept 2025.

Digital Transformation has raised the bar for speed, scalability and reliability. Modern applications are expected to run 24/7, deliver seamless experiences and adapt quickly to market shifts. But traditional, manual-heavy QA simply cannot meet these expectations.

Accessibility and localization often seem like separate disciplines, each with its own set of guidelines and goals.

In today’s data-driven software-as-a-service (SaaS) environments, the need for complete customer insights often requires fetching and sharing data that lives across multiple API endpoints. That’s why many of our customers want to use Confluent’s data streaming and integration capabilities to implement real-time API chaining—a technique that allows them to automatically follow relationships between APIs.