Systems | Development | Analytics | API | Testing

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!

For enterprises relying on SAP systems, ensuring smooth operation through updates, integrations, and daily operations is crucial. Manual testing of these complex systems increases time to market and the likelihood of human error. The solution? SAP testing automation, which enables faster, more reliable testing, ensuring your business-critical processes run smoothly without bottlenecks.

Unravel Data is the DataOps observability platform for the modern data stack. Unravel provides AI-powered recommendations for performance, cost governance, and quality so data teams spend less time firefighting and more time innovating. Let’s see how it works.

Modern data analytics platforms like Databricks have become indispensable for many companies. However Databricks costs can become unpredictable. This article gives four steps to help companies like yours predict Databricks costs and save your company time and money.

Adobe launched Adobe Experience Platform Federated Audience Composition, now generally available on Snowflake, allowing organizations to unlock seamless interoperability for marketers by integrating Snowflake's AI Data Cloud with Adobe Real-Time Customer Data Platform (CDP) and Adobe Journey Optimizer.

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.

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.

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.

The Domain Name System (DNS) is often described as the address book of the Internet. A and AAAA records map a human-friendly hostname like honeybadger.io to some machine-friendly IP address like 104.198.14.52. Other types of DNS records also exist; in particular, CNAME records are records that map a hostname to some other hostname, thereby delegating IP resolution to the latter.