Systems | Development | Analytics | API | Testing

In modern software development, the pressure to move fast is matched only by the need to get it right. Teams working within the software development lifecycle (SDLC) must constantly balance velocity and quality, ensuring releases are stable, secure, and performant. Traditional software development models often relied on manual verification and human intuition to validate releases; however, as systems have grown in complexity, guesswork is no longer sufficient to meet these rising needs.

Every year, Data Summit attracts data professionals to Boston for three days to exchange ideas and innovation. In 2025, the event was dedicated to data management trends, Big Data, business intelligence (BI), analytics, as well as emerging innovation in AI and machine learning. At the Data Summit, Sami Akbay, insightsoftware’s Head of Data Analytics Projects, hosted a session on the evolution of BI, the rise of generative AI, and the shift toward insight-driven workflows.

The evolving market landscape is driving an urgent need for a unified enterprise performance management (EPM) solution, as finance teams face increasing pressures from several fronts. Rapid technological advancements, heightened competition, and the growing complexity of global markets have made financial agility and real-time decision-making critical to maintaining a competitive edge.

We’re excited to announce the release of DreamFactory 7.1.1 — a milestone update that lays the foundation for AI-powered data integration while continuing to build on our mission of secure, no-code API generation.

I was reading about the gRPC recently and was wondering what all this about? Believe me, I was in the same boat not too long ago. I didn’t even know what exactly gRPC means before this. If you’re curious about how to inspect gRPC traffic, check out this guide on capturing gRPC traffic going out from a server. In this blog, I will walk you through everything I have learned about the game changing technology that is changing the world of distributed system.

In today's competitive landscape, data warehouses and data lakes are the essential platforms for business intelligence, analytics, and AI. While immensely powerful, these systems were traditionally designed for batch data processing, often leading to insights based on data that is hours or even days old. The primary challenge has always been the complexity of bridging the gap between real-time data streams, typically flowing through Kafka, and these analytical systems.