We’re back! That’s right, we have a new episode of Exception Perceptions to share with y’all. This is part 2 of of our Star Wars series, and this time we’re talking all about using errors to better understand user behavior.
Complex distributed systems run just about every service imaginable. Healthcare systems that monitor patient health, security systems, and financial systems are all mission-critical. Downtime, or lack of availability, loses money and can even put lives at risk. These systems must be monitored. Many measurements are useful to keep systems running with as little downtime as possible. One of those is Mean Time To Recovery. (MTTR.)
Across every industry and operational model we serve, customers tell us they’re struggling with finding the actionable signal in a sea of data. The systems and services teams run are growing more complex every year, and headcount never scales at the same rate.
Meet the Honeycomb Beeline for Ruby. Like our Beelines for Go and Node, it understands the common packages you’re using and automatically instruments them to send useful events to Honeycomb. Then once you’ve got a chance to explore your app’s behavior, you can add custom fields specific to your app with just one line of code.
We live in a world of big data, where even small-sized IT environments are generating vast amounts of data. Once an organization has figured out how to tap into the various data sources generating the data, and the method for collecting, processing and storing it, the next step is analysis.
Many enterprises are at the brink of digital transformation, which entails adopting new technologies that process a sea of both personal and enterprise-level data. Despite a surging number of innovations to prevent evolving cyber threats from hijacking that data, the sheer number of successful, high-profile data breaches and attacks recently highlight the insufficient security practices of organizations around the globe.
It’s no secret that Kubernetes is the leader when it comes to container orchestration platforms. Since its 2014 release, the open source project has taken the world by storm and become one of the biggest success stories in the open source community.
Picking the right tools to handle the deployment will depend on your environment. Since there are so many ways to deploy to Azure services, we won't be able to get into the weeds, but I will cover enough so that you can hopefully find the right deployment solution for you.
Amazon Elastic Container Service for Kubernetes (EKS), the latest addition to the AWS platform, is a cloud-based Kubernetes service that provides features for automated cluster management and maintenance. Whether you are migrating an existing Kubernetes cluster or deploying a new application to Amazon EKS, Datadog can help you monitor your container infrastructure and applications in real time.
Once you have your Elasticsearch running, you’ll likely eventually find that performance starts to suffer over time. This can be due to a variety of factors, including changes in the way you’re using your cluster to how much and what types of data are being sent in. In order to maintain your cluster, you’ll need to set up monitors to alert you to any warning signs so that you can proactively handle available maintenance windows.
