After spending hours or even days manually testing your software, you may be wondering whether or not there’s a better way. The good news? There is.

Modern organizations generate large amounts of logs from multiple data sources, creating significant challenges when it comes to analyzing the data and extracting useful insights at scale. Data scientists can tackle these challenges with help from Mosaic AI, which helps Databricks users build and deploy artificial intelligence (AI) and machine learning (ML) solutions.

In the early days of web application development, monolithic frontends and backends were perfectly fine. But the emergence of the cloud has changed expectations. Developers want to build applications that exchange information with APIs (Application Programming Interfaces). Business owners want faster development times. End users expect zero downtime. This new paradigm enabled the rise of service-oriented architecture (SOA) and, along with it, microservices and web services.

Composability is a growing trend in enterprise software development. It means designing your organization to be flexible so you can adapt to change in real time. Modularity lies at the heart of composability. Modular approaches have been around for decades, such as in service-oriented architecture (SOA) and microservices. But the rise of low-code platforms, cloud computing, and modern integration tools has made it easier to implement composable architectures at scale.

In the fast-changing landscape of mobile app development, delivering bug-free apps with a great user experience is essential. But crashes are inevitable, no matter how much developers try to avoid them. There are several mobile crash reporting tools that make the task of identifying, diagnosing and fixing these issues easy as pie.

In the fast-paced world of software development, ensuring top-notch quality is a must. One approach that has gained significant traction over the years is Exploratory Testing. Unlike scripted testing, exploratory testing focuses on real-time discovery, creativity, and problem-solving. In this article, we will understand exploratory testing, its benefits, techniques, and how tools like Keploy can support this process. Let’s get started!

The rise of generative AI models are spurring organizations to incorporate AI and large language models (LLMs) into their business strategy. After all, these models open up new opportunities to extract greater value from a company’s data and IP and make it accessible to a wider audience across the organization. One key to successfully leveraging gen AI models is the ability to share data.

Did you know that 70% of mobile app users will abandon an app if it takes too long to load, while 57% will consider switching to a competitor after just 3-4 negative interactions with a company’s website or mobile app?

Recap: This is the last part of our four chapters: It’s been a long time coming, but we’ve finally arrived at the fourth and final installment of our blog series. In this series, we’ve been peeling back the layers of Apache Kafka to get a deeper understanding of how best to interact with the cluster using producer and consumer clients. At a high level, a fetch request is comprised of two parts: Let’s dive in.