Operations | Monitoring | ITSM | DevOps | Cloud

For retailers and ecommerce store owners, your bottom line is always affected whenever your service is down, due to today's consumers expecting their digital interactions to operate around the clock. This is particularly crucial during spikes in traffic due to sales, like Black Friday or Cyber Monday.

Like physical architecture, the architecture running your business data — any and compute-intensive AI projects — is important. This data architecture governs a very important part of your business: how well users can translate raw information into real knowledge and actionable insights. Today, your data architecture is getting perhaps more attention than ever before. And that’s all thanks to usable AIs that now exist.

In today’s contemporary landscape, organizations produce more data than ever, which needs to be collected, stored, analyzed, and retained, but not necessarily in that order. Historically, most vendors’ analysis tools were also the retention point for that data. Still, while this may first appear to be the best option for performance, we have quickly seen it creates significant problems.

Here we go again! Multimodal AI is the “next big thing” in artificial intelligence technology. But what exactly does multimodal mean, and how does it differ from the AI models we’ve all grown familiar with? Let’s take a look.

The modern IT landscape consists of many distributed systems, which can pose a challenge if you are responsible for the end-to-end performance of these systems. As a platform engineer today, that is exactly what the job requires. You must juggle between dozens of tools to meet SLAs. This is why a modern solution is needed to bridge the gap between disjointed infrastructure and application stacks…and this is why the Splunk Observability platform was born.

Observability, in modern software engineering, has evolved into a paramount concept, shedding light on the intricate inner workings of complex systems. Three essential pillars support this quest for clarity: logging, traces, and metrics. These interconnected elements collectively form the backbone of observability, enabling us to understand our software as never before. Think of a system as a bustling city.