As organisations grapple with increasingly complex systems, applications, and infrastructure, the need for robust monitoring and analytics solutions has never been more critical. DataDog is a well known observability platform that has risen to prominence by addressing these challenges head-on.

Platform engineers can influence API development by following best practices and implementing DevOps design patterns.

Splunk’s recent update to its Machine Learning Toolkit (MLTK) is a good reason to spend a few paragraphs thinking through the links between Observability and machine learning. First, let us quickly review what Observability and machine learning are.

If you’ve been building web apps long enough, you’ve certainly read through your fair share of logs. One of the more painful parts of going through web app logs is getting them all to the same place. It’s only then you can review your logs and troubleshoot your applications. In this article, we’ll build a simple Go web app to send logs to SolarWinds® Loggly®.

Despite what you may have seen and heard, the intersection of logging, metrics and tracing does not tell the whole story about observability. Our systems emit telemetry, and those previously noted telemetry signals are considered the “three pillars” of observability. They’re all important, but by themselves, they aren’t observability. Many users I see day in and day out find themselves with broken observability even though they’re collecting those three pillars.

Platform Engineering is a hot topic these days. We’ve seen the hype around it in 2023, and I expect we shall see it becoming production-grade as we move into 2024. I wanted to look into this topic, and learn from those who’ve already implemented it at scale: the e-commerce hyperscaler Shopify. In the latest episode of OpenObservability Talks, I had the pleasure of hosting Aparna Subramanian, the Director of Production Engineering at Shopify.

“Is it worth it?” is probably the most common question customers ask business architects and value advisors. Whether it’s a software deployment or process improvement, customers want to be assured that the effort and risk of a project delivers real value. That is the question people in my line of work spend their days trying to answer. In many cases, the answer is complicated and requires a great deal of experience to explain.

The ability to extract meaningful insights from your wealth of organisational information is often the key differentiator between successful businesses and those struggling to stay competitive. Splunk, a renowned leader in the realm of data analytics and visualisation, has emerged as a powerful ally in this quest for actionable knowledge.

As data-rich solutions are important for many businesses, technical information can become overwhelming, especially regarding shared environments and multi-tenancy. In the world of Graylog, we understand these challenges and present the tools you need to keep your cluster running smoothly. Let’s dive into how you can effectively manage shared Graylog clusters.

Graylog is a log management and security platform, built on the Torch project, originally founded in 2009. It offers a set of log management and SIEM features. It does not offer metrics & traces as part of its offering, so we will only compare the Coralogix logging feature set.