Operations | Monitoring | ITSM | DevOps | Cloud

Choosing an excellent application performance monitoring tool is a challenging task. Nowadays, there are dozens of instruments, and it can be problematic to pick the right one. However, when looking into every given “top ten list”, New Relic vs. Datadog will always be there. At this point, instead of focusing on dozens of log management tools, let’s focus on some key ones. Comparing New Relic vs. Datadog offers a distinct perspective on how infrastructure monitoring should look.

Datadog and Splunk are among the most popular performance monitoring tools available on the market. If you’re looking for such a solution and looking to scratch one off your shortlist, look no further than this article. In this Datadog vs Splunk comparison, we will take a deep dive into everything each tool has to offer. We will point out their similarities and differences to help you decide which tool can meet your needs better.

Meta, the parent company of Facebook, has been fined a record €1.2 billion ($1.3 billion) by the European Union for violating its data privacy laws. The fine was issued by Ireland’s Data Protection Commission, which is Meta’s lead regulator in the EU, and is the largest ever levied under the EU’s General Data Protection Regulation (GDPR), which went into effect in 2018.

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.

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.