Operations | Monitoring | ITSM | DevOps | Cloud

Several years ago, there was little choice among performance monitoring tools. You had to deal with what the market offers. Datadog is one of the oldest solutions available and, thus, well-known. Yet, it is not without flaws, which might make people look for alternative solutions since the market is booming and new tools emerge regularly.

DevOps teams and security engineers use monitoring tools like Prometheus and Datadog to search for bugs and find any issues that might put an app or the entire IT infrastructure at risk. Better monitoring capabilities and aspects like event monitoring mean users can log data more effectively and engage in data collection leading to data visualization. These actions lead to infrastructure metrics, which allow experts to conduct timely analysis and prevent an app from crashing.

A little over two years ago, Splunk announced a revolutionary feature that would simplify the life of Splunk Cloud administrators by providing APIs that enable self-management and self-administration of Splunk Cloud Stacks. No more waiting for support tickets to be written, emailed, prioritized and then executed.

By 2025, more than 180 zettabytes of data will have been created and captured around the globe. With the influx of data in this digitized world, the process of data aggregation has become an essential tool for businesses. It’s a way to take large amounts of separate data from different sources and combine them into one collective body.

With Cribl Stream, our customers are experiencing choice and control over their data that would have been a pipe dream (or maybe I should say a pipeline dream) before. The ability to get the right data to the right destination in the right format is extremely powerful. Stream can optimize the data being sent to expensive destinations; you can remove unnecessary or redundant fields, drop unnecessary events, or even pull valuable metrics from verbose logs. Optimizing your data has a few benefits.

Growth of cloud computing and the preference for data-driven decision-making have led to a steady increase in investments in observability over the years. Telemetry data is recognized as not only critical for maintaining a company’s infrastructure, but also for aiding security and business teams in making informed decisions. However, just increasing investment in observability technology is not enough.

Remember the first time you were at a wedding, or a party and you learned about dances like The Electric Slide? You know, those dances with a clear structure and steps to follow, which were a huge help to someone who was slightly challenged on the dance floor, like me? All you had to do was learn a few simple steps, and you could hang with even the best dancers.

SQL and NoSQL are two database technologies widely adopted by many organizations for different use cases. Both technologies share the common goal of efficiently processing and managing data. Still, there are some significant differences. This article compares SQL and NoSQL, exploring their key differences in terms of language, structure, scalability, properties and support. We’ll also discuss examples, pros and cons and the most suitable application areas for each database type.