Kafka Summit 2019 (San Francisco)

Friday, Oct 4, 2019 | 2 minute read

Ricardo Ferreira

This week I had the honor of being one more time in the most exciting conference for Apache Kafka in the world, known as Kafka Summit. Only this time was different because I could contribute a lot for this conference.

My first contribution was building and recording a demo that was shown in the second day of the conference when Priya Shivakumar was talking about Confluent Cloud. I have received some good feedback about that demo, which of course made me feel really proud.

BTW, Priya did a great job explaining what Confluent Cloud is and why it is the most successful managed service for Apache Kafka out there.

I also had the chance to host a couple tracks during the 1st and 2nd day of the conference. I hosted the Event-Driven Development track, as well as the Core Kafka track, where I get the chance to announce some really amazing speakers.

Finally, I had the chance to speak at the conference. My session was “Being an Apache Kafka Developer Hero in the World of Cloud” and I think I did a pretty good job while presenting it. My talk was all about showing how easy Confluent Cloud is and how fast it can help you develop event streaming applications. The way I decided to showcase this was by showing a demo from scratch, where I would bring nothing to the stage other than a GitHub repository link and internet connection to build this up from the ground.

Though I have had a few hiccups while cloning the repository (I still think the internet was blocking traffic somehow) I was able in less than 30 minutes create a Confluent Cloud cluster in AWS, deploy an entire application also in AWS and let the audience play with the application. The application itself was the Pac-Man game, and I am pretty sure that I nailed picking that game because everybody liked playing it.

Here is a set of pics that had been taken from my Twitter feed:

Since I have announced myself as a Deadpool fan – the audience asked me to wear my mask for a picture. Hey, can you say no to that?

Going to Kafka Summit is good for several reasons, but the most important of all is the community. Being able to interact, exchange experiences, help and being helped, as well as engaging with others, is what makes Kafka Summit a huge success.

See you next year Kafkateers!

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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.

Public Speaking

I’ve been speaking at conferences since 2008 and doing it full time as part of my work with DevRel since 2018. My talks go deep on the systems I build with: distributed systems and event streaming, AI engineering and vector search, and the data infrastructure that has to hold up when the demo ends and production begins. 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.

Ricardo Ferreira presenting on the main stage at AI DevWorld
On the main stage at AI DevWorld

You can find my upcoming and past talks on my speaking calendar. Recordings also live on my YouTube channel, and the code I write for talks, demos, and workshops is on my GitHub.

Consulting and Professional Services

If you’d like to hire me as a consultant for your projects, speak at your event, or lead a hands-on workshop for your team, contact me at riferrei@riferrei.com. I can understand the scope of your request and provide a free estimate.

Who am I?

I work at the intersection of AI, data infrastructure, and distributed systems, turning complex technology into things developers can understand and products users love.

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

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

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