Confluent Cloud Tools is Alive

Friday, Nov 9, 2018 | 2 minute read

Ricardo Ferreira

It gives me great pleasure to announce that Confluent Cloud Tools is officially released as a Confluent’s repository. The link to this repository is:

https://github.com/confluentinc/ccloud-tools

In a nutshell, the aim of this project is to allow developers to unfold the true agility that Confluent Cloud can provide –allowing them to focus on writing code instead of wasting time with manual and tedious configuration steps.

Very often developers use tools from Confluent such as Schema Registry, REST Proxy, Connect, KSQL and Control Center – along with their Kafka clusters. This may work fine when they are working On-Premise and making use of the integrated experience that Confluent Platform provides – whether if it is running Confluent Platform locally and managing things via the CLI or; if it is by using the built-in Docker containers.

However, things can be quite stressful when they need to implement code on these tools in the Cloud. Though Confluent Cloud can take away the burden of managing the brokers and Zookeeper by themselves – these tools still need to be manually provisioned, configured, fine tuned and secured.

Networking problems, ports that don’t bind correctly, visibility between subnets, scaling in/out processes across availability zones, bastion servers – are some of the concerns that rises when developers try to provision these tools by themselves.

Well… not anymore 😊

© 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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