Application monitoring is a well-established discipline that dates back decades and remains a pillar of software management strategies today. However, as software environments and architectures have evolved, monitoring techniques have needed to evolve along with them. That’s why many teams today rely on distributed tracing to glean insights that they can’t gather from application monitoring alone.

It's one thing to detect a cyber attack. It's another to know what the attackers are trying to do, which tactics they are using, and what their next move is likely to be. Without that additional information, it's difficult to defend effectively against an attack. You can't reliably stop an attack if you are unable to put yourself in the mindset of the attackers. This is why threat intelligence plays a critical role in modern cybersecurity operations.

Security operations is now a critical business function tasked with securing digital transformation initiatives, to effectively mitigate evolving attacks and expanding attack surfaces, handle complexity and tool proliferation while teams are continuing to be virtual and distributed.

Telegraf is a server-based agent for collecting all kinds of metrics for further processing. It’s a piece of software that you can install anywhere in your infrastructure and it will read metrics from specified sources – typically application logs, events, or data outputs.

In a recent experiment with my colleagues, I polled them about the following: “What would they do if the lights went out as you worked at night?” Besides identifying the funny and who-you-want-in-case-of-an-emergency responses, most of my colleagues checked to see if the problem might be broader than their own home.

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.

It’s essential to choose the right tool for the job. I have an old, sturdy screwdriver that I use for lots of odd DIY jobs around my house, like cleaning gutters, opening paint cans, and general maintenance on my lawnmower. However, when I’m performing an upgrade on my computer, a large, rusty screwdriver isn’t the best tool to remove the screws anchoring my motherboard.

If your team falls into the majority of organizations that use NGINX – which remains the world’s most popular Web server – to host websites and Web applications, monitoring NGINX usage, performance, and transactions is critical for maintaining a positive end-user experience. Keep reading for tips on doing so. This article identifies the most important metrics to monitor for NGINX in order to understand key usage and performance trends within NGINX transactions.

We are excited to join AWS for the launch of Amazon CloudWatch Metric Streams; a fully managed, scalable, and low latency service that streams Amazon CloudWatch metrics to partners via Amazon Kinesis Data Firehose. AWS and Sumo Logic customers can now leverage AWS Kinesis Firehose for Metrics Source for streaming CloudWatch metrics into their Sumo Logic accounts, to help simplify the monitoring and troubleshooting of AWS infrastructure, services, and applications.

Microservices and serverless are both important topics in the world of cloud-native computing. Yet, although serverless functions and microservices architectures often go hand-in-hand, they’re distinct technologies that fill different roles in modern software environments. Here’s an overview of what microservices and serverless are, how they relate to each other, how they are different, and why you may or may not wish to deploy a serverless microservice.