At the beginning of 2021, a brand new data team was assembled to build a real-time data platform for Kong’s SaaS platform, Konnect. Our mission is to provide top-notch real-time API analytics features for Konnect customers. V1 platform architecture The initial backend consisted of three main components: an ingestion service to handle incoming telemetry requests, an intermediate message queue, and a real-time OLAP data storage.

When automating a web application, we occasionally run across the NoSuchElementException exception, which is thrown when the element being interacted with cannot be located. The main reason for this error is that the element that has to be interacted with is present on the page but takes some time to load and show up for the user. You can see how this could become a significant problem during automation and cause our scripts to fail.

When it comes to data-driven business decisions, research and analysis are often used interchangeably. However, these terms are not synonymous, and understanding the difference between them is crucial for making informed decisions.

Live chat is a baseline for customer service, as well as connecting users of two-sided marketplaces, live auction sites, and social networks. And it’s easy to see why.

End to end testing (E2E testing) is a software testing technique that evaluates the functionality and performance of an application by testing it from start to finish, under real-world conditions. By simulating the live settings, E2E testing validates the behavior of not only the overall application but also its sub-systems from the end user's perspective.

A while back we predicted that.NET Core would be the next big thing, offering developers many options in application development. Indeed, there is huge demand for developers that know this technology. But how does it differ from the.NET Framework, and what do you need to know to use them both effectively? Today, we’ll contrast.NET Core vs. .NET Framework to help you choose which one to use for your next project.

How can you find time to drive efficiency across your organization when there is so much digital work to be done? This is one area where hyperautomation and digital transformation initiatives intersect. Yet, just like other technology trends, our understanding of hyperautomation and its value has become convoluted. Let’s demystify some of the gray areas surrounding what hyperautomation technologies and tools can help you accomplish as you shape automation strategy and select tools.

What’s the right way to manage growing volumes of enterprise data, while providing the consistency, data quality and governance required for analytics at scale? Is centralizing data management in a data lake the right approach? Or is a distributed data mesh architecture right for your organization? When it comes down to it, most organizations seeking these solutions are looking for a way to analyze data without having to move or transform it via complex extract, transform and load (ETL) pipelines.

To effectively run tests of all the important test-cases and guarantee that all the criteria are being met to the fullest extent feasible, testers must do more than just run the software through its paces; they must also manage, gather, and retain massive volumes of data. These testing datasets are used as input by test cases, from which anticipated and unexpected system behavior may be determined.