Moving to a scalable, distributed microservice architecture poses a great deal of challenges for any organization. It gets harder to understand the system and pinpoint where errors originate. Logs get much messier, and stitching together a coherent picture of a particular request can be time-consuming or downright impossible. Distributed tracing can help with all of that.

Jaeger is a popular distributed tracing project hosted by the Cloud Native Computing Foundation (CNCF). In the Elastic APM 7.6.0 release we added support for ingesting Jaeger traces directly into the Elastic Stack. Elasticsearch has long been a primary storage backend for Jaeger. Due to its fast search capabilities and horizontal scalability, Elasticsearch makes an excellent choice for storing and searching trace data, along with other observability data such as logs, metrics, and uptime data.

Here at Sumo Logic, we share a lot of thoughts about managing data at scale, and the innovative ways we help customers address their unique use cases. It’s not just about analysis of logs. In this article, I will talk about another important observability signal: distributed traces. I will share a few observations about how we at Sumo think about the future of adoption of distributed traces, a very important concept, taking from our own experience.

Lumigo’s Auto-Tracing allows you to implement distributed tracing on your Lambda functions with 3-clicks and no manual code changes. If you’ve already decided to move to a serverless infrastructure, you probably understand the importance of monitoring your AWS Lambdas and what it might entail. For the few out there that are still wondering what monitoring AWS Lambda means, I’ll break it down for you in a couple of steps.

Monitoring began by using software agents to capture data from infrastructure, operating systems, and applications. These agents would collect metrics and events from these systems to understand the health of the underlying system and the applications. This is what infrastructure monitoring is today.

With microservices becoming popular, tracing is increasingly more important in debugging production software. In this post, we take you through a step by step guide on setting up Jaeger over Kubernetes with Cassandra storage.

In this article we are going to explain Jaeger integration with the OpenTelemetry collector, describe differences between these two and have a look at Kubernetes deployment via Jaeger Operator. OpenTelemetry collector is a vendor-agnostic service for receiving, processing and exporting telemetry data. In the Jaeger project we have decided to deprecate the Jaeger collector and migrate its functionality to an implementation based on OpenTelemetry collector.