It’s Monday morning, and Sam woke up to a gentle sound on his phone. It’s not an alarm but his new financial pal—a voice-activated AI assistant. This friendly voice, tailored to his financial goals, provides a personalized morning briefing of his accounts, highlights upcoming bills, and even suggests budget-friendly options for his breakfast. After a busy workday, Sam stared at his evening coffee.
In today’s digital landscape, ensuring the optimal performance of your applications is paramount for delivering seamless user experiences. However, identifying potential bottlenecks is not easy. This analysis is one of the single most important activities in performance testing, but it’s often the most difficult one, and a misstep can cause significant setbacks.
Before shedding light on metadata management, it is crucial to understand what metadata is. Metadata refers to the information about your data. This data includes elements representing its context, content, and characteristics. It helps you discover, access, use, store, and retrieve your data, having a wide spread of variations. Metadata of an image. Image by Astera. Let’s look at some of the metadata types below.
It’s hard to know who to trust for automated testing tools. With so many options, deciding which one will fit your specific needs can be tough. That’s why we’re breaking down the top tools in the industry, their advantages and disadvantages, the tests they’re used for, their price points, and more. We’ll also share user reviews and give you our recommendations to help you find the perfect fit for your business needs.
The pharmaceutical industry generates a great deal of identifiable data (such as clinical trial data, patient engagement data) that has guardrails around “use and access.” Data captured for the intended purpose of use described in a protocol is called “primary use.” However, once anonymized, this data can be used for other inferences in what we can collectively define as secondary analyses.
For even the most tech- and data-savvy individuals, working with the levels of raw data produced by businesses today is overwhelming. Well-executed data dashboards solve this problem by eliminating the noise and drilling down to just the data points necessary at that moment. A data dashboard's dynamic nature helps your team get the most up-to-the-minute information right when they need it.
Hello, Katalon users and software testing enthusiasts! Today, we’re diving into a common challenge that many of you have encountered: navigating through a web page using the ‘Tab’ key. This blog post is inspired by a real-life scenario shared by a Katalon user on our community forum.
Data aggregation is the process of combining and summarizing data from disparate sources into a cohesive dataset. It prepares data for analysis, making it easier to obtain insights into patterns and insights that aren’t observable in isolated data points. Once aggregated, data is generally stored in a data warehouse. Then, you can leverage it to gain a holistic perspective on your operations and market trends, design effective risk management practices, and make more informed decisions overall.
Time series data is everywhere. It captures how systems, behaviors and processes change over time. Enterprises across industries, such as Internet of Things (IoT), financial services, manufacturing and more, use this data to drive business and operational decisions. When using time series data to perform analytics and drive decisions, it’s often necessary to join several data sets.