Down to the Rabbit Hole with Pulsar I/O

Pulsar Summit NA 2021 – Virtual

Jun. 2021

Ricardo Ferreira

Slides

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Abstract

Apache Pulsar is a distributed messaging and streaming platform that stores messages durably and scalably into its persistent store, making it an attractive technology to store business data. However, merely storing the data is not enough. To make this data useful, the platform must also provide ways to ingest new data and send existing data elsewhere. While developers can build applications for this using the client libraries, the reality is that most of them don’t want to spend time writing code for repeatable tasks such as — reading data from a database and storing it into Pulsar. Reason why Pulsar abstracts away things like this by providing a connector-based framework called Pulsar I/O. This talk will provide an overview of how the Pulsar I/O framework works and a deep dive into troubleshooting things — from identifying when the connector is not working correctly to more elaborating investigations that may be useful for debugging purposes. It will give you the required tools to master how to ingest and export data into and out of Pulsar effectively.

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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