In a survey by the Harvard Business Review, 87% of respondents stated their organizations would be more successful if frontline workers were empowered to make important decisions in the moment. And 86% of respondents stated that they needed better technology to enable those in-the-moment decisions. Those coveted insights live at the end of a process lovingly known as the data pipeline.
Today, we are pleased to announce the availability of Tricentis Test Automation (TTA) for SAP integrated with SAP Cloud ALM. This release is the result of three years of close collaboration between Tricentis and SAP, and the result brings the best of application lifecycle management and test automation to SAP customers.
As we continuously improve Choreo's capabilities, we're excited to introduce a significant new addition: Deployment Tracks. This empowers users to achieve backward-compatible API releases, ensuring a smoother experience for API publishers and API consumers alike. This transition will entail minor user interface (UI) changes in the Choreo console for your existing components.
Various factors can impede an organization's ability to leverage Confluent Cloud, ranging from data locality considerations to stringent internal prerequisites. For instance, specific mandates might dictate that data be confined within a customer's Virtual Private Cloud (VPC), or necessitate operation within an air-gapped VPC. However, a silver lining exists even in such circumstances, as viable alternatives remain available to address these specific scenarios.
Today, we are excited to announce the support of Dockerfile based deployments in general availability. You can now deploy any GitHub repository that contains a Dockerfile across all our locations worldwide. It can be used to deploy APIs, full-stack applications as well as workers with no extra cost. Building and deploying using Dockerfiles offers more flexibility: you can deploy any kind of application, framework, and runtime, including with custom system dependencies.
Database testing is the process of evaluating the accuracy, reliability, and performance of a database system. Its purpose is to ensure that the data stored there is consistent, valid, and can be correctly manipulated for business needs. The components to be tested are usually database schema, tables, and database triggers. Testers leverage SQL queries, data comparison tools, automation frameworks, or load testing tools to examine the data integrity, validity, security, performance, and structure.
There is no doubt about it: Artificial Intelligence (AI) and Machine Learning (ML) has changed the way we think about software testing. Ever since the introduction of the disruptive AI-powered language model ChatGPT, a wide range of AI-augmented technologies have also emerged, and the benefits they brought surely can’t be ignored. In this article, we will guide you to leverage AI/ML in software testing to bring your QA game to the next level.
