Systems | Development | Analytics | API | Testing

Here, we cover what a monorepo is, how it differs from a monolith, what the benefits of monorepos are, and why companies like Google use a monorepo.

Discover Go's unique approach to error handling! In this article, you'll master best practices with practical examples and learn how to wield the power of defer, panic, and recover to create robust and resilient Go applications.

While larger companies generally have the budgets to build a software development team and continuously expand upon it through training and new hires, the average company doesn’t have the same luxury. With that said, IT outsourcing is a growing industry and there are plenty of pros and cons.

Artificial intelligence (AI) promises outsized gains in business efficiency. And since organizations live or die by their processes, integrating AI into the picture through AI process optimization can help boost ROI with end-to-end process automation and enhancements. This blog will explore five ways AI can be used to improve and optimize process efficiency. But first, let’s explain what AI process optimization means.

Many API developers and companies struggle to find ways to easily set up systems to unlock revenue from their APIs. Some are simple but not customizable while others are complex and require massive engineering effort to actually get it all running. To make things easier, Moesif created the Billing Meters feature which allows for massive customization with a minimal amount of code and engineering effort.

Customizing exceptions is usually not a common concern during software development. But if you catch an error in an observability tool and can't correctly and quickly identify the problem, you may need more information and details about an exception. This article will show you how to customize exceptions in Ruby and mitigate potential future problems due to a lack of error information. Let's dive straight in!

I’m a firm believer that banks rarely invest to drive revenue and elevate customer service. But I recently hosted a roundtable discussion in London with Appian, a process automation platform company, where leaders from mainstream and challenger banks in the UK gathered to discuss the challenges facing financial institutions when adopting new technology.

Data management in the modern enterprise requires skill and stamina. With the mountainous rise in data volume and the complexity of managing disparate data sources across a wide variety of legacy systems and hybrid cloud environments, it can seem virtually impossible to successfully climb past enterprise data obstacles to success. However, data fabric technology eliminates some of those obstacles. Is it the answer for your data strategy?