We’re going to investigate the basic strategies to debug an application created with Unity, from logs to breakpoints, during the development stage. Once an app is in production we can switch to using Bugfender, and we’ll explain this too. To illustrate this tutorial we have created a custom app called The Bugfender Game, a variation of the popular Flappy Bird. Unity is a cross-platform game engine that gives users the ability to create games and experiences in 2D, 3D, VR and AR.

The first lesson you learn as you start to work around the DevOps field is that being optimistic is not a good virtue. While that might seem overly pessimistic, let me explain. We plan our architecture to fit our needs, deal with edge cases, scale our applications up and as wide as we see fit, and with all of that, we still always expect for the unexpected to happen. As engineers, we are expected to deal with that unexpected.

Product lifecycle management is all about the dynamics between requirements and their fulfilment (e.g., the ability to test and prove that a requirement is fully covered, as defined during the product planning phase). Understanding this relationship, and respecting it unconditionally, allows great products to achieve world-class quality and stability. For the majority of products, the combination of people, processes, and technology is triggering different forms of changes to the original plan.

Data integration has been around for decades in some form or fashion, as organizations are always looking for ways to combine their enterprise data and collect it in a centralized location. The most commonly used and dominant type of data integration is ETL (extract, transform, load). ETL first extracts data from one or more source systems, transforms it as necessary, and then loads it into a target warehouse or data lake.

Most organizations today have some form of digital transformation strategy in the works. But a large share of these projects fail. Why is this? IT specialists will tell you that apart from misalignment between the goals of IT and business leadership, the main obstacle to a successful digital transformation is the adherence to obsolete data silos.

From Silicon Valley startups to established corporations, more and more companies are shifting to a composable enterprise architecture. And it is easy to see why. The world of business technology is growing fast and in unpredictable ways. Businesses need to stay competitive by keeping pace with — or better yet, ahead of — emerging tech. Composable enterprises allow for this flexibility by establishing the groundwork for integrating new applications and functionalities.

What’s the fastest and easiest path towards powerful cloud-native analytics that are secure and cost-efficient? In our humble opinion, we believe that’s Cloudera Data Platform (CDP). And sure, we’re a little biased—but only because we’ve seen firsthand how CDP helps our customers realize the full benefits of public cloud.

You’ve probably heard it more than once: Machine learning (ML) can take your digital transformation to another level. It’s a pie-in-the-sky statement that sounds great, right? And while you’d be forgiven for thinking that it might sound too good to be true, operational ML is, in fact, achievable and sustainable. You can get the very kind of ML you need to increase revenue and lower costs. To help teams work smarter and do things faster.

Trying to integrate your marketing data sources? Here are the fundamental differences between Fivetran and Supermetrics as marketing ETL tools.

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