Building Observable Streaming Systems with OpenTelemetry

Berlin Buzzwords – Virtual

Jun. 2021

Ricardo Ferreira

Slides

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Abstract

Building streaming systems is a popular way for developers to implement applications that react to data changes and process events as they happen. It is an exciting new world that technologies like Apache Pulsar made available for anyone to use. But all this goodness doesn’t come for free. One of the challenges of this type of architecture is that its distributed nature makes it hard and sometimes even impossible to identify the root cause of problems quickly. That is why distributed tracing technologies are so important. By gluing together disparate services into a single and cohesive transaction, developers can provide to the operations team a way to pragmatically observe the system and to quickly identify the root cause of problems such as slowness and unavailability. This talk will explain how to implement distributed tracing in Pulsar applications using OpenTelemetry—an observability framework for cloud-native software. A demo will be used to clarify the concepts.

Video

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© 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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