A typical workflow in a modern software dev project often looks like: Build code, put the built artifact into a container image, put the container image into a registry and deploy to Kubernetes. Each step has it’s own requirements and pitfalls alike. The overall goal is most often to bake those steps into easily repeatable pipelines and enable a high degree of automation and standardization.
Dockerfiles seems to be the choice with the highest adoption when it comes to containerizing code artefacts. However most Dockerfiles (and also most sample's of Dockerfiles you find in tutorials) do not exploit it to the full potential.
There are a lot of improvements to be done in how to build and optimize for Java-based applications.
The talk will give deeper insights by comparing (simple and multi-stage) Dockerfiles to Cloud-Native Buildpacks (buildpacks.io/paketo.io), Google’s JIB and Source2Image under the evaluation criteria of build time, build size, standardization, robustness, memory configuration and security. The examples and live demo will have certain focus on Java-based frameworks (Spring Boot, Quarkus, Micronaut), but coverage of other languages will also be included and highlighted.

The lab covers the implementation and usage of OpenTelemetry into Python and Java-based application. Each participant will get a dedicated environment.
The exercise include

• the instrumentation of a polyglot microservice application
• auto-instrumentation vs. manual instrumentation
• evaluating the collected traces, logs and metrics
• configuring a collector
• analysing the results in Jaeger

This lab is made for developers seeking a pragmatic understanding of OpenTelemetry's immediate benefits. Walk away with practical skills that can be implemented right away in your projects, and elevate your observability game in 180 minutes.