APIs play a crucial role in SaaS product’s ability to communicate with internal and external applications. APIs have revolutionized the ways in which applications communicate, exchange data, and how software developers and providers structure their systems and products. But managing the large flow of data and information while ensuring data protection and minimal downtime is no easy task.

This post is part of a series on becoming a secure API-first company. For a deeper dive, check out the eBook Leading Digital Transformation: Best Practices for Becoming a Secure API-First Company. As APIs have become mission-critical, securing them against threats is crucial. APIs are an attractive target for attackers, and a single vulnerability can expose an organization's most sensitive information assets. To properly secure APIs, we have to move beyond basic perimeter defenses.

Adding new features or enhancements to an app is a good thing – unless the changes trigger an unexpected problem. To protect against this risk, QA teams and developers can leverage automated regression testing, which offers a fast and efficient means of confirming that software upgrades or changes don't introduce bugs into applications.

Imagine you’ve just started a new job working as a business analyst. You’ve been given a new burning business question that needs an immediate answer. How long would it take you to find the data you need to even begin to come up with a data-driven response? Imagine how many iterations of query writing you’d have to go through. In this scenario, you also have reports that need updating as well. Those contain some of the biggest hair-ball queries you’ve ever seen.

The rise of generative AI (gen AI) is inspiring organizations to envision a future in which AI is integrated into all aspects of their operations for a more human, personalized and efficient customer experience. However, getting the required compute infrastructure into place, particularly GPUs for large language models (LLMs), is a real challenge. Accessing the necessary resources from cloud providers demands careful planning and up to month-long wait times due to the high demand for GPUs.

Databox is mostly known for its business analytics and visualization capabilities. As a single-product company for the majority of its existence, it was developed in a way that catered to just one product. As the company began its path as a startup, it was convenient to keep the architecture simple and flexible to quickly adapt to change and align direction if needed to ensure business success.

In our previous How to Design a Dashboard in Yellowfin: Part Two article, the Yellowfin Japan Team covered how to design a complete image of a dashboard, from start to finish, in our visual-based canvas interface. In this article, the team will demonstrate the necessary process to create the final dashboard.

The heart of SaaS innovation in the modern era is API technology, connecting one digital tool to another seamlessly. As APIs have taken center stage, the term “API Developer Portal” has become more than just a buzzword—it’s a crucial element in the realm of software development and the easiest way to ensure seamless connectivity.

Customer service is an art—and a science. It isn’t just a transactional function. It's also a relationship building activity that’s deeply tied to physiological responses in our brains. And the stakes are high: these interactions shape the neural architecture of customer loyalty. So can you use AI for customer service? Let’s explore that question.

As the connecting blocks of modern data communication, webhooks and APIs play pivotal roles, yet their distinctions are often blurred. Delving into their underlying mechanisms, functionalities, and use cases, after this blog post you will fully master the difference between webhooks and APIs. By unraveling the intricacies of these technologies, this explorative post serves as a resource for anyone looking to navigate their landscape of data integration and communication protocols.