Hello, Katalon users and software testing enthusiasts! Today, we’re going to delve into a common issue that has been discussed in our vibrant Katalon Community: the ‘End of File Error’ exception when validating PDFs.

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.

In today's data-driven world, the ability to seamlessly connect, manage, and manipulate vast amounts of data is paramount for businesses and developers alike. Graph API stands at the forefront of this technological frontier, offering robust tools that facilitate complex data interactions within applications. This powerful API provides a framework for accessing and integrating data points in an intuitive and effective manner, supporting dynamic data structures across various platforms.

Last year, we introduced the Connect with Confluent partner program, enabling our technology partners to develop native integrations with Confluent Cloud. This gives our customers access to Confluent data streams from within their favorite applications and allows them to extract maximum value from their data.

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.

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.

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.

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.

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.

The service mesh architecture pattern has become a de facto standard for microservices-based projects. In fact, from the mesh standpoint, not just microservices but all components of an application should be under its control, including databases, event processing services, etc.