To ensure a great user experience, whether that’s attending a live virtual event, chatting with friends or collaborating on a document, you need to ensure that the core realtime works in any situation and that you provide a feature-rich application. Disruption, downtime or lags result in poor realtime user experiences.

As you could probably guess, we discuss debugging a lot at Rookout. We tackle many methods and strategies throughout all our blogs and resources. However, it only recently occurred to us that we don’t have a direct face-off of the two major methods of modern debugs: live debugging vs. remote debugging. At first glance, this might sound like semantics, but the two terms have come to embody specific methods and protocols. Both are powerful, but there is some fine print to look at also.

The management of data assets in multiple clouds is introducing new data governance requirements, and it is both useful and instructive to have a view from the TM Forum to help navigate the changes.

It’s that time of year - back to school, back to books, and our annual must-read books for data and analytics leaders. Given the pace of change in our industry, continuous learning is a must, whether through networking, podcasting, or reading. To cull this year’s list, I focused mainly on books published in the last two years with the themes of data, analytics and AI. I scoured lists and reviews on Amazon, solicited ideas from social networks and got to reading.

In government agencies around the world, large enterprise legacy systems are what stand in the way of desperately needed modernization programs. Such legacy systems can be large enterprise resource planning (ERP) implementations or custom-built applications using complex codebases. Simply put, they are not supportable, upgradable, and do not provide the rich user experience customers have become accustomed to.

How can you ensure data quality and security across your data analytics pipeline? With data governance – the exercising of authority and control over your data assets. It includes tracking, maintaining and protecting data at every stage of the lifecycle.

Have you ever experienced the problem where your code is broken in production, but everything runs correctly in your dev environment? This can be really challenging because you have limited information once something is in production, and you can’t easily make changes and try different code. Speedscale production data simulation lets you securely capture the production application traffic, normalize the data, and replay it directly in your dev environment.

In our last post, we began comparing the PHP frameworks Laravel and Symfony. We went over an overview of each framework, and also what particular advantages each one has. In this post, we’ll continue by discussing the following: Finally, we’ll go over which framework is right for your particular use case.

It’s quite natural to become sidetracked when balancing the many activities involved in software development. You must keep track of deadlines, application goals and your piece of the project, while handling internal and external communications and producing good code. That’s where software development tools come into play. There are a variety of applications available that are designed to make tasks easier for a developer.

Fintech is the perfect example of how emerging, disruptive technologies are rocking a traditional industry: the banking sector. Fintech, a portmanteau of financial technology, is its own industry, changing the banking world and forcing it to evolve. Most financial institutions are now using some kind of fintech, from simple banking apps to entire platforms aimed at promoting open banking.