Systems | Development | Analytics | API | Testing

Earlier this year, Yellowfin celebrated its 20th anniversary. While we have been around for a while, some things remained constant - our focus on customer and innovation. Yellowfin was borne out of the frustrations felt by our founders, and millions of other information workers, in accessing the data they need to do their jobs effectively, a frustration just as relevant today as it was 20 years ago.

Imagine you're building an e-commerce application. You want to use Django and bring all the benefits of that framework. But there are issues. Django's service-oriented architecture doesn’t play well with the event-driven nature of what you need to build. You need to automatically send a welcome email when a new user registers, instantly update inventory levels when an item is purchased, and notify users when their orders are shipped.

Every application, web or non-web-based, has to deal with data. Even though there are static websites, where data is somehow embedded, most sites and applications rely on dynamic data stored somewhere and somehow. What testing can you do around data handling? Can you use exploratory testing to uncover problems related to data? What type of problems? In this article, you’ll get some guidelines for your exploratory testing sessions around data.

As someone who’s been closely following the evolution of software testing, this year saw the shifts in methodologies and processes. One of the most significant findings is the strong adoption of Agile and DevOps methodologies, reshaping how testing is integrated into the software development lifecycle.

As one of the most popular programming languages, Java has stood the test of time. The versatile and powerful language has a vast ecosystem and offers numerous advantages and opportunities, whether you’re a beginner or an experienced developer. This post covers Java benefits, various use cases, and why monitoring your Java applications is crucial to application success.

In the contemporary business landscape, the integration of artificial intelligence (AI) into business processes is no longer a futuristic concept—it's a present-day reality. Organizations across various industries are leveraging AI to streamline operations, enhance decision-making, and drive innovation.

Move your data lakes and warehouses to Google Cloud with a new migration incentive program as well as enhanced BigQuery migration services and tools.

Being inundated with tools and platforms to track, analyze, and optimize your products can be extremely overwhelming, and most of the time, you just get lost in the process of managing all the tools, without really achieving anything actionable. While having access to specialized tools can be beneficial, managing multiple analytics solutions can become counterproductive.

Thoroughly examine the project's scope, complexity, and specific needs. Identify key stakeholders, their expectations, and potential challenges. Ask yourself questions like: By understanding these aspects, you can tailor the template to address the project's specific demands.

As software systems grow in complexity, the importance of comprehensive testing cannot be overstated. However, writing unit tests is often a time-consuming and repetitive task leading to developer fatigue. In this blog we will over come the manual unit test development and move towards LLM driven/AI assisted unit tests.