Take me Down to the Paradise City Where the Metric is Green and Traces are Pretty

SRECon 2021 Virtual

Oct. 2021

Ricardo Ferreira

Slides

Abstract

Observability is a software discipline that goes back to when virtually any problem could be solved by tailing web server logs. But the world has changed, and systems these days are comprised of different services running in their own stacks who cooperatively build up what we understand as the end-to-end architecture. Thus, observability had to evolve as well. Today we have OpenTelemetry—an observability framework for cloud-native software. OpenTelemetry provides the tools, APIs, and SDKs to create a reusable, robust, and non-vendor-driven observability strategy. But the reality is that most developers are still confused about the lines that separate OpenTelemetry from the past and which parts of the framework are stable enough to be used in production. This talk will explain how OpenTelemetry works and provide examples in Java and Go to illustrate the APIs you can use to produce traces and metrics.

Video

© 2018 - 2026 Ricardo Ferreira

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Open Source

I contribute to LangChain4j, an idiomatic open source Java library for building LLM-powered applications on the JVM. Recent work includes adding native vector search embedding stores so developers can build RAG, recommendation engines, and AI memory systems.

I also ported RedisVL to Go, an open source, AI-native client that brings vector search, semantic caching, LLM memory, semantic routing, rerankers, and an MCP server to the Redis ecosystem for Golang developers.

Speaking

I’ve been speaking at conferences since 2008 and doing it full time as part of my work since 2018. Some of the events I’ve spoken at include AWS re:Invent, Microsoft Ignite, Google Cloud Next, KubeCon, Oracle OpenWorld, QCon, Strange Loop, Kafka Summit, Pulsar Summit, JavaOne, DevNexus, JFokus, JNation, and All Things Open.

Recordings of my talks live on my YouTube channel, and the code I write for talks, demos, and tutorials is on my GitHub.

Who am I?

I work at the intersection of distributed systems, AI, and data infrastructure, turning complex technology into things developers can understand, adopt, and build with.

Lately, that means hands-on AI engineering: building vector search, semantic caching, agent memory, and RAG into the data layer; contributing to LangChain4j and RedisVL for Golang; and figuring out how to make AI agents secure enough to ship.

The AI-native work isn’t a pivot. It draws on the same systems-design foundation I’ve built for 20+ years, moving data fast, at scale, close to compute, watching where systems break; now applied to vectors and agents. I have worked on event streaming with Apache Kafka and Flink at AWS and Confluent; observability at Elastic; as well as RDBMS, NoSQL, and Big Data at Oracle. That foundation is exactly what separates AI demos that work on stage from AI systems that survive production.

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