Thursday, Sep 30, 2021
TestContainers is a popular framework that allows Java developers to create tests that depend on backend systems such as Elasticsearch. It automatically creates a container instance for the said backend, preventing developers from manually spinning up their dependency instances. Also, it enables them to treat those instances as disposable, meaning that once the tests finish executing, the underlying containers created for the instances are destroyed automatically.
The good news for Go developers is that there is a version of this for them as well. It is called TestContainers-Go. In this post, I will walk you through creating a simple test that connects with Elasticsearch using this framework.
Friday, Jul 16, 2021
Most developers deploy their Elastic (Elasticsearch and Kibana) clusters manually by copying the bits to the target system or semi-manually using containers. This might work well if you need to maintain few clusters for your company, where likely you know the name of each one from the top of your head. However, this approach doesn’t scale very well if you have to spin up clusters on-demand for seasonal requirements.
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.
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.
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.