One of the building blocks of messaging is, you guessed it, messages! But there are different kinds of messages: Commands and Events. So, what’s the difference? Well, they have very distinct purposes, usage, naming, ownership, and more!

In 2021, the Biden administration issued an executive order outlining that the government and private sector need to work together to combat cyberthreats and improve the nation’s collective cybersecurity stance. As cyberattacks become more common and more costly, the United States — like other nation-states — needs to do everything it can to prevent attacks and rapidly respond to them when they occur, which requires modernizing its approach to incident response.

Do you want to use Kafka? Or do you need a message broker and queues? While they can seem similar, they have different purposes. I’m going to explain the differences, so you don’t try to brute force patterns and concepts in Kafka that are better used with a message broker.

Though organizations are projected to pump $4.4 trillion into IT spending this year, the supply of developer talent has struggled to keep pace, and many companies are having a hard time hiring workers. This is due to the fact that today’s top-performing developers understand their value and are increasingly asking for more money and better benefits when considering job offers.

Do you remember the classic board game where you have to go back and forth with your opponent deducing which characters on the board you’ve each selected? It’s still played by children today, and unfortunately by healthcare teams as well. Every day, healthcare teams are forced to play a game of “Guess Who?” is on-call if they do not have systems in place for role-based messaging.

Many people wonder what the difference is between monitoring vs. observability. While monitoring is simply watching a system, observability means truly understanding a system’s state. DevOps teams leverage observability to debug their applications or troubleshoot the root cause of system issues. Peak visibility is achieved by analyzing the three pillars of observability: Logs, metrics and traces.

The volume of data that IT systems generate nowadays is overwhelming, and without intelligent monitoring and analysis tools, it can result in missed opportunities, alerts, and expensive downtime. However, with the advent of Machine Learning and Big Data, a new category of IT operations tool has emerged called AIOps. AIOps can be defined as the practical application of Artificial Intelligence to augment, support, and automate IT processes.

Your release management process is one of the most critical processes in your organization’s toolkit. An excellent release management process can accelerate software release workflows, allowing your team to deliver software consistently and predictably and ensuring that your customers have an optimal brand experience. But release processes can have weaknesses that slow your team down and can even introduce risk to every release.

Artificial intelligence for IT operations (AIOps) is an umbrella term for the use of big data analytics, machine learning (ML) and other artificial intelligence (AI) technologies to automate the identification and resolution of common IT issues. The systems, services and applications in a large enterprise produce immense volumes of log and performance data. AIOps uses this data to monitor assets and gain visibility into dependencies within and outside of IT systems.