Systems | Development | Analytics | API | Testing

As your organization’s data operations mature, you will need to upgrade your infrastructure to fulfill new needs and requirements.

At three full days and nearly 4,000 registered attendees, Gartner Data & Analytics Summit 2023 in London was the largest data and analytics summit in EMEA to date. That much interest in data and analytics thrills us, of course — but it was also exciting because it was one of our first major events following the announcement that Talend has been acquired by Qlik. There was a lot to talk about!

Manual testing involves the hands-on evaluation of software or applications, without relying on automated testing tools or pre-written test scripts. Testers simulate user interactions to uncover bugs, defects, and elements that hinder the user experience. By immersing themselves in the software, testers can identify and report issues that may have otherwise gone unnoticed. On the other hand, automated testing allows for the automatic execution of tests using software frameworks.

Test strategy is a high-level plan consisting of principles that guide the overall software testing process. It provides a structured approach to the entire QA team, guiding them toward achieving testing objectives in the most efficient way. In this article, we’ll walk you through the step-by-step process of developing a thorough test strategy, explain the differences between the test plan vs test strategy, and even provide a template for you to follow.

One of the main targets we try to achieve with Loadero is to make load and performance testing as accessible and simple as possible. As a major step toward making the test creation easier, a whole new test builder was designed and developed, with major benefits: Whether you have never used Loadero before, or are an existing user that has gotten used to the old test builder, this blog post will guide you through setting up a test in Loadero via our new test builder.

Performance. Reliability. Cost-effectiveness. Unravel is a data observability platform that provides cost intelligence, warehouse optimization, query optimization, and automated alerting and actions for high-volume users of the Snowflake Data Cloud. Unravel leverages AI and automation to deliver realtime, user-level and query-level cost reporting, code-level optimization recommendations, and automated spend controls to empower and unify DataOps and FinOps teams.