Being alerted to an issue with your application before your customers experience undue interruption is a goal of every development and operations team. While methods for identifying problems exist in many forms, including uptime checks and application tracing, alerts on logs is a prominent method for issue detection. Previously, Cloud Logging only supported alerts on error logs and log-based metrics, but that was not robust enough for most application teams.

Apache Kafka® is one of the most popular streaming and messaging platforms, commonly used in a pub-sub (publish-subscribe) model, where consumer software applications send data via messages that producer software applications can consume. Teams use Kafka for a variety of use cases, including monitoring user activity, sending notifications, and concurrently processing streams of incoming data such as financial transactions.

Last year we published a popular paper on how to calculate the true cost of an Elasticsearch, or ELK (for Elasticsearch, Logstash, Kibana) stack environment. The paper helps readers calculate their overall annual cost of ownership for their ELK environment, and reveals how the cost burden of ELK is much higher than anticipated for most customers. That paper clearly hit a nerve — it’s been, by far, our most downloaded piece of content.

AIOps is a DevOps strategy that brings the power of machine learning to bear on observability and system management. It’s not surprising that an increasing number of companies are now adopting this approach. AIOps first came onto the scene in 2015 (coincidentally the same year as Coralogix) and has been gaining momentum for the past half-decade. In this post, we’ll talk about what AIOps is, and why a business might want to use it for their log analytics.