Manufacturing has historically been laggards in its adoption of emerging technologies as business processes, from the production line to back-office operations, have inherently been tied to legacy applications. But that is rapidly changing. Faced with competitive pressures, and driven by technological enhancements across broad sectors of the economy, today’s manufacturing leaders are seeking new ways to improve productivity, reduce downtime, and streamline operations and the supply chain.

Running software uses computer memory for data structures and executable operations. How this memory is accessed and managed depends on the operating system and the programming language. Many modern programming languages manage memory for you, and Ruby is no different. Ruby manages memory usage using a garbage collector (also called gc). In this post, we’ll examine what you, a Ruby developer, need to know about Ruby’s gc. Use the links below to skip ahead in the tutorial.

As the world is adapting to new and unforeseen circumstances, many of the traditional ways of doing things are no longer. One significant effect of this is that the world has gone almost completely virtual. Whether it’s Zoom happy hours and family catch ups or virtual conferences, what used to be in-person has digitized.

APIs (Application Performance Interfaces) have changed the way we build applications, and they’ve changed how applications communicate with each other. Essentially, APIs give enterprises the agility to rapidly and cost-effectively incorporate new services and updates into their applications and IT infrastructures.

Apache YuniKorn (Incubating) is a standalone resource scheduler that aims to bring advanced scheduling capabilities for Big Data workloads onto containerized platforms. Please read YuniKorn: a universal resources scheduler to learn about the rationale and architecture. Since the time of our last post, we are delighted to update that YuniKorn was accepted by the Apache incubator in Jan 2020!

It was three years ago, just after the Gartner Magic Quadrant (MQ) came out, that everything in customer success changed at Yellowfin. We had made it into the MQ again but we weren’t positioned where we believed we should be. We were a product-driven company that had been first to market for many of the functionalities that have since become expected in what BI vendors offer today.

While digging through data, Anna spots an interesting trend - some customers buy 3 times more than others. A segment of super-high spenders? This could make it rain for the company! She rushes to her boss, to show them the data, only to hear: “Yeah, I know. We have a bug, which inserts every order three times in the database. Were you not paying attention during our daily meeting?” Aw-kward.

This blog post is a guide to help you write Gatling scripts in order to load test web applications efficiently. It follows our second Gatling Simulation scripts parameterization article. We will continue to load test a fake e-commerce, and so we are going to improve our Virtual User to make it browse the store in a more humanly way. We start where the previous blog post ended, with a simulation script that uses a CSV feeder and a Regular Expression extractor to visit dynamic pages of the pet store.

To quote the study, “For every USD 14 million delivery by the software development and QA team, a hidden USD 3 million was being spent on data management.

Working from home is the new norm for teams all over the world. For software development teams, adapting to the new working environment has been going on for more than one month now. To further understand the challenges of working (or testing) remotely, we conducted a quick survey on the state of working from home for the testing community.