If you’re tired of writing endless lines of repetitive code in RestAssured just to test your APIs, you’re not alone. API testing shouldn’t feel like pulling teeth, but let’s face it—REST Assured can make the process boring and unnecessarily time-consuming. But what if you could leave that grind behind? In this guide, we’ll show you how to make the switch to Keploy, a smarter, zero-code way to test your APIs.

The first step is often the hardest, and in software testing, this is especially true. When presented with a system, how do you go about and decide what to test? Standing in the middle of the unknown, we need guidance, and learning about test case design techniques is a good place to start. In this article, we’ll show you: Let’s dive in!

Companies are under pressure to deliver faster innovation, enabled by cloud-based data analytics and AI. In order to deliver faster business value, data teams are looking to achieve speed and scale through data and AI pipeline performance and efficiency. A recent MIT Technology Review Insights report finds that 72% of technology leaders agree that data challenges are the most likely factor to jeopardize AI/ML goals.

Every enterprise is modernizing their business systems and applications to respond to real-time data. Within the next few years, we predict that most of an enterprise's data products will be built using a streaming fabric – a rich tapestry of real-time data, abstracted from the infrastructure it runs on. This streaming fabric spans not just one Apache Kafka cluster, but dozens, hundreds, maybe even thousands of them.

Gen AI has the potential to bring immense value for marketing use cases, from content creation to hyper-personalization to product insights, and many more. But if you’re struggling to scale and operationalize gen AI, you’re not alone. That’s where most enterprises struggle. To date, many companies are still in the excitement and exploitation phase of gen AI. Few have a number of initial pilots deployed and even fewer have simultaneous pilots and are building differentiating use cases.

Picture this: It's Black Friday, and you're circling a packed mall parking lot. Every space is taken, and cars are lined up waiting for spots. You keep circling, but there’s just no place to park and you run out of gas. When you see a java.lang.OutOfMemoryError it’s just like what you experienced in that overcrowded parking lot. The Java Virtual Machine (JVM) has run out of space to "park" new objects in memory. Now here's the thing about Java: it loves objects. It can't get enough of them.

In the world of data management, clearinghouses often serve as intermediaries, enabling secure data transactions across different platforms and ensuring compliance with industry regulations. However, as data management needs evolve, businesses are increasingly looking for alternatives to clearinghouses that offer more flexibility, control, and advanced features.

APIs are essential to modern applications, but managing access and security policies can be complex. Traditional access control mechanisms can fall short when flexible, scalable, and fine-grained control over who can access specific resources is needed. This is where OPA (Open Policy Agent) steps in. OPA provides a unified framework for consistently defining and enforcing policies across microservices, APIs, Kubernetes clusters, and beyond. Consistent policy management is essential for enterprises.

More often in today’s society of growing ‘entrepreneurs’, almost all are developing Minimum Viable Products MVPs that breathe life into their concepts. A shocking but true statistic reveals that 90% of startups fail. One big reason for this is the poor execution of a key tool in the startup world: the minimum viable product (MVP). One of the reasons for this failure is the lack of tests conducted appropriately.