Distributed tracing has been growing in popularity as a primary tool for investigating performance issues in microservices systems. Our recent DevOps Pulse survey shows a 38% increase year-over-year in organizations’ tracing use. Furthermore, 64% of those respondents who are not yet using tracing indicated plans to adopt it in the next two years. However, many organizations have yet to realize just how much potential distributed tracing holds.

Monitoring cloud-native systems is hard. You’ve got highly distributed apps spanning tens and hundreds of nodes, services and instances. You’ve got additional layers and dimensions—not just bare metal and OS, but also node, pod, namespace, deployment version, Kubernetes’ control plane and more. To make things more interesting, any typical system these days uses many third-party frameworks, whether open source or cloud services.

AWS CloudFormation is a service that enables you to create and provision AWS infrastructure deployments predictably and repeatedly. This helps you leverage AWS products such as EC2 instances, Amazon Elastic Block Store, Amazon SNS, Elastic Load Balancing, and Auto Scaling to build highly reliable, highly scalable, cost-effective applications in the cloud – without worrying about creating and configuring the underlying AWS infrastructure.

It’s been a busy couple of months at Logz.io. We’ve added new features, made critical updates, and added a slew of integrations. Those integrations run the gamut from observability and security services, to cloud tools and container orchestration. Let’s take a quick look at what’s new and what’s coming up at Logz.io.

“Security is always seen as too much until the day it’s not enough.” – William H. Webster, former FBI Director As we all know, every year, thousands of new vulnerabilities are discovered, requiring organizations to patch operating systems, update applications, and reconfigure security settings throughout the entirety of their IT environments, including the cloud.

Engineering teams hoping to gain full-stack observability into their environment need access to the relevant logs, metrics, and traces generated by their cloud infrastructure and applications. Accessing the relevant data quickly is essential – not just because it is more convenient, but because faster engineers are also business-critical for many organizations.

OpenTelemetry is the recommended path today for instrumenting applications with tracing in a standard, vendor-agnostic and future-proof way. In fact, OpenTelemetry (nicknamed OTEL) encompasses all three pillars of observability: tracing, metrics, and logs. The tracing element of the specification is now stable with the rest following. This is innovative stuff! You can read more on OpenTelemetry and the current release state on this guide.

We just announced the creation of a new RemoteWrite SDK to support custom metrics from applications using several different languages. This tutorial will give a quick rundown of how to use the Java SDK. This SDK – like the others – is completely free and open source, and is meant to apply to any output destination, not just Logz.io.

In this tutorial, we will go through a working example of a Python application auto-instrumented with OpenTelemetry. To keep things simple, we will create a basic “Hello World” application using Flask, instrument it with OpenTelemetry’s Python client library to generate trace data and send it to an OpenTelemetry Collector. The Collector will then export the trace data to an external distributed tracing analytics tool of our choice.

We all know the importance of cataloging, organizing, and breaking down the data in your logs. That process, parsing, makes information easier to find and simplifies subsequent analysis. Now, with Logz.io’s upgraded self-parsing tool, custom parsing rules, and log organization is easier than ever. What’s important is parsing that data out correctly. The better parsed, the easier to query.