Sumo Logic is a cloud-based log management and analytics service that leverages machine-generated big data to deliver real-time IT insights. We’re excited to share that you can now easily integrate Catchpoint and Sumo Logic, giving you a number of fantastic benefits. The integration involves pushing data from Catchpoint to Sumo Logic using Webhooks and then query the data to build visualizations. Why do we use Webhooks?

Today, we are pleased to announce a partnership with Confluent to jointly develop and deliver an enhanced product experience to the Kafka-Elasticsearch community. Kafka is — and has been since the very early days — an important component of the Elastic ecosystem.

When migrating to Kubernetes and re-architecting your applications into containers, logging is a critical piece to consider. The twelve-factor app methodology has a section dedicated to logging and outlines the importance of not worrying about routing and storage of your logs. As a best practice, applications running in containers should rely 100% on standard output (STDOUT). Unfortunately, getting logs from applications that do not write to STDOUT is non-trivial and has many things to consider.

Link analysis, which is a data analysis approach used to discover relationships and connections between data elements and entities, has many use cases including cybersecurity, fraud analytics, crime investigations, and finance. In my last post, "Advanced Link Analysis: Part 1 - Solving the Challenge of Information Density," I covered how advanced link analysis can be used to solve the challenge of information density.

Nowadays, most applications we build are composed of microservices and distributed in nature. In such a setup, communication between these microservices is crucial, but can, unfortunately, cause some headaches. The first thing I check when I’m troubleshooting a bug in production is inter-service communication. Having a reliable tool at your disposal to take care of this can reduce a lot of stress. RabbitMQ, a hybrid messaging broker, is one such tool.

Today, we’ll cover some of the ways you might find quite useful in your everyday work. We’ll go through some of the logging best practices in AWS Lambda, and we will explain how and why these ways will simplify your AWS Lambda logging. For more information about similar topics, be sure to visit our blog. Let’s start with the basics (and if you have the basics covered, feel free to skip ahead): How does logging work with AWS Lambda?

With more people working from home, the threat landscape continues to change. Things change daily, and cybersecurity staff needs to change with them to protect information. Threat hunting techniques for an evolving landscape need to tie risk together with log data. Within your environment, there are a few things that you can do to prepare for effective threat hunting. Although none of these is a silver bullet, they can get you better prepared to investigate an alert.

In my first post in the Kubernetes Logging Simplified blog series, I touched on some of the ‘need to know’ concepts and architectures to effectively manage your application logs in Kubernetes – providing steps on how to implement a Cluster-level logging solution to debug and analyze your application workloads. In my second post, I’m going to touch on another signal to keep an eye on: Kubernetes events.