My Talk @ Kafka Summit SFO 2019

Monday, Sep 9, 2019 | 3 minute read

Ricardo Ferreira

We are less than one month away from the next edition of the world’s best conference about Apache Kafka known as Kafka Summit, and I would like to detail a bit what is going to be the focus of my talk Being an Apache Kafka Developer Hero in the World of Cloud.

https://kafka-summit.org/sessions/developer-hero-cloud/

As you can read in the description, this talk is all about Confluent Cloud, where I will be showing the first steps to start working with event streaming applications using this awesome Apache Kafka as-a-service product from Confluent. However, the interesting part here is how I am going to do that.

Since a picture is worth a thousand words, I will start explaining this one:

That is right: I will be using the infamous Pac-Man game as a building block to teach how to develop event streaming applications using Confluent Cloud. Attendees of this talk will get to play with the game using their phones since the game will be deployed in a cloud provider. As they play with the game, events will be emitted to Kafka topics in near real-time.

There will be two types of events being emitted:

  • Events about the game containing current score, number of lives and levels;
  • Events about when the user loses a game, usually known as game-over;

To make things even more interesting, I will be creating a fairly complex processing pipeline using KSQL , that will compute aggregated statistics about each user’s game. I will create this pipeline step-by-step so the attendees can get the chance to see how the pipeline evolves from simple Kafka topics to a set of streams that will suffer lots of operations such as rekeying, counting, grouping, joins – and finally becoming a table that will hold the computed statistics from each user’s game.

This table will serve the game’s scoreboard… an always-updating, live, near real-time display that lists all game’s statistics per user, where users will be ordered based on their game’s performance:

This scoreboard will be implemented using Golang, which will act as a microservice that consumes the table created. This showcase a typical event streaming application where microservices will be acting upon the data that is being held-and-computed by Kafka, therefore showing an end-to-end application , far from a simple hello world.

Throughout the 45 minutes of the talk, I will be doing a live coding session with the attendees where I will be executing the following tasks:

  1. Creating a cluster from scratch in Confluent Cloud.
  2. Testing connectivity with a simple producer and consumer.
  3. Cloning the GitHub repo that contains the Pac-Man game.
  4. Walking through the code and explaining the architecture.
  5. Setting up the necessary credentials to deploy the game.
  6. Deploy the game in the cloud provider using Terraform.
  7. Ask attendees to play with the game to generate events.
  8. Build the scoreboard using a pipeline written in KSQL.
  9. Run the Golang consumer to verify data correctness.
  10. Destroying the development KSQL Server created.
  11. Creating a KSQL application on Confluent Cloud.
  12. Re-creating the scoreboard in the KSQL application.

…and of course, I will be answering as many questions attendees may have. To ensure I will have the time to execute all these tasks, I will bebringing near-zero slides for this talk. Therefore, attendees can expect to spend more time with the code part and less time with boring slides.

Looking forward to seeing all of you in shinny San Francisco, though I cannot promise if the weather will be that shinny!

© 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.

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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